problem solving strategies to overcome obstacles that hinder academic goals

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5 Strategies for Academic Success: Using Your Strengths

These five tips show you how to keep a clear head and have a successful semester.

Rebecca Bakken

The start of the semester can be a hectic time. You’re juggling your career, classes, family, and friends. Deb Levy , a certified life and business coach, and a Harvard Extension career workshop leader, offers five tips in this video to help you succeed in your coursework.

Know your strengths.

It’s human nature to want to correct weaknesses. But knowing your strengths and how to use them effectively can have a much more substantial effect on success and well-being. So how can you reframe your thinking?

According to Deb Levy, the field of positive psychology offers many useful tools. One in particular—the Character Strengths Test from VIA Institute on Character—can help you gauge your strengths and weaknesses. The test ranks users’ character strengths from strongest to weakest, allowing for an objective view into where you excel and where you may need work.

Once you know what your strengths are, you can play to them. But it’s also important to know that sometimes strengths need to be tempered.

“Every strength if overused becomes a deficit,” says Levy.

For instance, someone who ranks highly in humor might run the risk of making an insensitive or inappropriate comment that could damage relationships.

Making a plan to bolster weaknesses while remaining conscious of strengths can be a great strategy to ensure not just academic success, but personal fulfillment.

Set specific goals.

Achieving your goals depends heavily on how well you can manage your time. Levy recommends making a priority pie that maps out how you’ll divide your time over the course of a semester.

“When you say yes to becoming a student, you have to say no to other things,” she says. “So goal-setting requires a strategic plan for the semester. Students who do better in general are the ones who take time to plan.”

Your priority pie should reflect all your personal, professional, and academic endeavors. For example:

Your priority pie should include not just classes and your work day, but also time for family, studying and homework, and self-care like going to the gym or getting a regular massage.

Levy stresses that the best goals are specific, personal, and flexible.

Prioritize happiness.

Feeling good about what you’re doing and why you’re doing it is the best way to ensure success. According to Levy, happiness often leads to success, but success on its own may not lead to happiness.

As such, prioritizing your own wellness is key to reaching your goals. Levy says well-being  consists of positive emotions, engagement, meaning, and achievement.

“By nature of being in school people are already prioritizing well-being. They’re getting engaged, working on accomplishments,” says Levy.

Read our blog post on work-life balance

Aside from making time for yourself, you can practice building positivity.

One exercise that Levy recommends is writing down three good things at the end of each day. These can be things you’re proud of, things you’re grateful for, or things that simply bring a smile to your face.

Studying subjects that give your life purpose or meaning can also be beneficial.

“People who connect meaning to their goals are more motivated,” says Levy

Be resilient.

Even with a good plan, obstacles will arise. How well you deal with those obstacles depends on your perspective.

In resilience coaching, Levy often refers to the work of psychologist Carol Dweck. Her research identifies two basic mindsets: fixed and growth. Fixed mindsets view mistakes or setbacks as insurmountable. Growth mindsets view them as opportunities for positive change.

If you lean toward a fixed mindset, the good news is that it’s not permanent. No one falls into one mindset 100 percent of the time. Training your brain to see opportunity where you once saw a roadblock is possible.

“Give yourself permission to be human,” Levy says. “Predict you’re going to make mistakes.”

One way to build resilience is by preparing for obstacles with implementation intentions , which are if–then plans designed to help people achieve goals. For example, “If I can’t get the financial aid I need, then I will reallocate money from my vacation or entertainment budgets.”

Setting these intentions gives you a default answer that helps you stick to your plan without having to deliberate or make a snap decision.

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Make time to recover.

Rather than avoiding stress altogether, Levy recommends setting aside time to mentally and physically recover.

As a student, you may sometimes fall into a “stretch zone,” where you’re extending yourself to accommodate for different obligations. Periods of stress can actually be positive and motivating if they expand your perception of what’s possible.

But it can lead to chronic stress when you don’t build in time to recover.

Viewing your eight hours of sleep every night as sacrosanct can go a long way toward staving off chronic stress. So before you pull another all-nighter, think about the effects it may have on you the next day.

Taking breaks, setting aside time for meals, and enjoying recreation can help fuel you and keep you on course to achieve your goals.

For more information from Deb Levy on balancing academics with life, check out How to Set Goals and Achieve Balance—In and Outside the Classroom .

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Overcoming Academic Challenges: A Guide for College & University Students

From effective studying to budgeting and time management, it’s normal for students to feel overwhelmed. Learning how to set realistic goals and take breaks is key for physical and mental health. It’s important not to be afraid to ask for help when needed and to take advantage of different student services available on campus.

In this blog, we’ll aim to break down the process of thriving in college and university and look at how to overcome academic challenges.

Why Struggling in College or University Is Normal and How to Overcome It

If you’re struggling in college or university, don’t worry—you’re not alone. In fact, it’s normal to struggle at some point during your academic career. The important thing is to find ways to overcome your challenges and succeed in spite of them.

Here are some tips to help you beat the struggle and succeed in college or university:

Don’t be afraid to ask for help. If you’re struggling with a particular subject or assignment, reach out to your professor or a tutor for help. Don’t try to tough it out on your own – getting some extra help can make all the difference.

Stay organized and on top of your work. One of the best ways to avoid struggle is to stay ahead of it. Keep a close eye on your assignments and deadlines, and make sure you’re staying on top of your work. A little bit of planning can go a long way.

Take care of yourself. It’s important to take care of yourself both physically and mentally. Make sure you’re eating right, getting enough sleep, and taking breaks when you need them. Taking care of yourself will help you stay focused and motivated, even when things get tough.

Many students in higher education struggle with academic challenges, such as:

• Getting used to a new setting (life on campus).
• Time management.
• Achieving goals (or setting them).
• Managing stress.
• Asking for help, and more.

If you’re struggling in college or university, don’t despair—there are ways to overcome your challenges and succeed in spite of them. By following these tips, you can beat the struggle and achieve your academic goals.

Transitioning to University Life

It’s no secret that transitioning to university life can be a challenge. But with a little planning and some helpful advice, you can make the transition with ease. Here are a few tips to help you beat the struggle:

First, get organized. Make a list of all the things you need to do before classes start, such as buying textbooks and supplies and set aside time each day to complete these tasks. This will help you stay on top of things and avoid feeling overwhelmed.

Second, reach out and get to know who can help, especially if you find yourself in a new town far away from home . Get to know your professors. Introduce yourself during their office hours or after class. They can be a great resource for information and advice, and they’ll be more likely to remember you when it comes time for grades.

Finally, don’t be afraid to ask for help. There are plenty of resources on campus, such as tutoring centers and writing labs that can help you with your academic challenges. Be sure to attend student support center events. Many help you transition to higher education with ease. Don’t hesitate to seek out help when you need it.

With these tips in mind, you’ll be well on your way to a successful transition into university life.

Effective Studying: Develop Your Routine

Getting good grades in higher ed can be tough, but there are some fool-proof ways to overcome academic challenges. The first thing to nail is effective studying skills. You need to find a study method that works for you and stick to it. Whether that means studying in short bursts throughout the day or cramming everything in the night before, find what works and stick to it.

Another key to success is to stay organized. Keep a planner and write down all of your upcoming assignments and tests. If the school has online tools and resources to help with this, use them. This will help you track everything and make sure you don’t miss anything.

Finally, don’t be afraid to ask for help when needed. There are plenty of resources on campus that can help you if you’re struggling with a class. Talk to your professor, student services, and even a tutor or learning center if you need extra help.

Time Management: Tips to Make the Most Out of Your Time

If you feel like you’re struggling to keep up with your workload, you’re not alone. Time management is a skill that takes practice to become perfect. But once you’ve mastered it, it can make a difference in your academic success. Here are some tips to help you get started:

Make a schedule and stick to it

One of the best ways to become more efficient with your time is to plan out your days in advance. Sit down at the start of each week and map out what you need to do. Then, block out time in your schedule for each task. Be realistic about how long each task will take, and don’t forget to factor in time for breaks. Once you’ve created your schedule, do your best to stick to it.

Break down big projects into smaller tasks

When you’re staring at a huge project, it can be overwhelming and tempting to procrastinate. But if you break the project down into smaller tasks, it will seem much more manageable. Plus, you’ll be able to celebrate each time you complete a small task, which will motivate you to keep going.

Use a timer

If you find yourself getting distracted, try using a timer. Set it for a specific amount of time that you want to work, and then focus on your task until the timer goes off. Once the timer goes off, take a short break before setting it again. This technique can help you stay focused and make better use of your time.

Cut distractions

When you’re trying to focus on a task, cut anything that might distract you. That means putting away your phone, closing out of social media, and finding a quiet place to work. By eliminating distractions, you’ll be able to focus on what you’re doing and get it done faster.

Take care of yourself

It’s important to take care of yourself both physically and mentally if you want to be successful in college. That means getting enough sleep, eating healthy meals, and taking breaks when you need them. When your mind and body are well-rested and nourished, you’ll be able to think clearly and focus on your studies.

Setting Goals: How to Set Realistic Goals and Achieve Them

Setting goals is a great way to stay on track and motivated in college. However, it can be difficult to set realistic goals that you will actually achieve. Here are a few tips to help you set realistic goals and achieve them:

Set specific and achievable goals. This means that you define your goals, and they are realistic. For example, rather than setting a goal to “get better grades,” try setting a goal to “raise your GPA by 5 this semester.”

Make a plan of action. Once you have set your goal, it is important to make a plan for how you will achieve it. This may include making a study schedule, meeting with your professor, or joining a study group.

Set a deadline. Having a deadline for your goal will help you stay on track and motivated to achieve it. Make sure that your deadline is realistic, and give yourself some wiggle room in case you need it.

Hold yourself accountable. One of the best ways to achieve your goal is to hold yourself accountable for it. This may mean keeping a journal of your progress. Set up a weekly check-in with yourself. You can find an accountability partner, student advisor, or tutor to help you with this.

Be flexible. Life happens and sometimes our best-laid plans fall through. If this happens, adjust your plan and keep working towards your goal.

Attending university is an exciting time. It can also be tough to stick to a budget, but it’s worth it in the long run.

There are a few key things to keep in mind when budgeting for school. First, make sure you have a realistic understanding of your income and expenses. Second, create a budget that you can stick to. And third, don’t be afraid to ask for help if you need it.

There are a lot of resources available to help you with budgeting for school. Talk to a student advisor or the financial aid office, or look online. Whatever you do, don’t give up!

Dealing With Stress: Tips to Deal With Stress in a Healthy Way

When it comes to stress, we all deal with it in different ways. Some of us get anxious, others of us get angry, and some of us shut down. But no matter how we deal with it, stress is a part of life that we all have to deal with.

There are a lot of different tips and tricks out there for dealing with stress, but not all are equal. Here are a few of my personal favourite tips for dealing with stress in a healthy way:

Get moving. Exercise is a great way to release tension and clear your head. Even a quick walk around the block can help you feel better.

Connect with loved ones. Spending time with loved ones can help you feel supported and loved, which can be a great stress reliever.

Take some time for yourself. Make sure to schedule some “me” time every day, even if it’s for a few minutes. This can be anything from reading your favorite book to taking a relaxing bath.

Practice relaxation techniques. There are lots of different relaxation techniques out there, so find one that works for you and practice it. Examples include deep breathing, progressive muscle relaxation, and visualization.

Seek professional help if needed. If you’re finding that you’re struggling to cope with stress on your own, don’t be afraid to seek professional help from a therapist or counsellor. Reach out to the student success center and get connected with a counsellor and mental health services.

Access Student Success Services

If you’re struggling in college or university, don’t worry—you’re not alone! Many students face academic challenges at some point during their post-secondary studies. The good news is that there are plenty of student services available to help you overcome these challenges.

Academic advising: Academic advisors can help you plan your courses, choose a major or minor, and understand university policies and procedures. They can also provide guidance if you’re struggling academically.

Tutoring: Most colleges and universities offer free tutoring services for students who need extra help in specific subjects. Take advantage of this service if you’re having trouble understanding course material or keeping up with assignments. You can also access university-level tutors, like the ones at TutorOcean, who support students to achieve excellent results with ease.

Writing center: Writing in higher education is different. It takes time to understand how to write in the style required by your teachers. If you’re struggling with writing papers, the writing center can help. Tutors can provide feedback on your writing and help you improve your skills.

Counselling services: If you’re feeling stressed, anxious, or depressed, counselling services can help. Counsellors provide support and guidance to help you cope with academic challenges and other life stressors.

There are many other student services available on most college and university campuses. If you’re not sure where to start, your academic advisor can point you in the right direction. Don’t be afraid to ask for help—it’s one of the best ways to overcome academic challenges.

The academic journey is not always an easy one. There will be times when you feel like you’re struggling to keep up. But don’t worry, we’ve all been there. The important thing is not to let these challenges get the best of you.

Let’s take a look at the key tools to help you overcome academic challenges in college/university:

• Remember, the struggle is real and it’s normal.
• If you’re new to university or college, attend and take part in new student resources and events.
• Build your time management skills.
• Seek support from the student success center and access student services.
• Work with a tutor, there’s a tutor for every subject .

By following these tips, you’ll be well on your way to overcoming any academic challenges that come your way. Don’t be afraid to ask for help when you’re struggling with something. Schools incorporate technology to help students succeed. There are plenty of resources available on campus, such as tutoring services and the library.

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Effective interventions for students struggling academically: A comprehensive guide

Key takeaways.

• Interventions for students struggling academically are crucial to support their academic success.
• Effective academic interventions and support services can empower struggling learners on their academic journey.
• Addressing the social-emotional needs of struggling students can help remove barriers to academic success.

In this comprehensive guide, we will explore a range of effective interventions and support services that can help struggling students thrive in their academic pursuits.

Understanding the Challenges Faced by Struggling Students

When it comes to supporting struggling students, it's essential to understand the challenges they face. By recognizing these difficulties, you can design effective   academic intervention programs   that cater to their unique needs.

Some common challenges that struggling students face include:

• Difficulty grasping new concepts, leading to falling behind in class
• Lack of motivation or interest in learning
• Difficulty with time management and organization skills
• Weak foundational skills, such as reading and writing
• External factors, such as financial or emotional stress, that impact academic performance

To address these challenges, it's crucial to use effective   student intervention techniques   that can provide targeted support. Some common   academic intervention programs   include:

• One-on-one tutoring:   This intervention provides focused, individual attention to the student, allowing for personalized support and guidance.
• Classroom accommodations:   Teachers can make adjustments to the classroom environment and teaching methods to cater to the student's learning styles and unique needs.
• Assistive technology:   Tools such as text-to-speech software or graphic organizers can enhance accessibility and provide additional support to struggling learners.
• Mentorship programs:   Pairing struggling students with a mentor who can provide academic and emotional support can be effective in increasing motivation and engagement.

By understanding the challenges faced by struggling students and utilizing effective interventions like these, you can support their academic success and help them reach their full potential.

How are intervention plans made for students with academic or behavioral challenges?

Read article

Individualized Instruction and Personalized Learning Plans

If you're looking for   academic success interventions   to support struggling learners, individualized instruction and personalized learning plans are excellent strategies. These   educational intervention strategies   can cater to the unique needs of each student, providing targeted support that maximizes their potential for success.

When designing personalized learning plans, educators can tailor the curriculum, teaching methods, and learning goals to each student, ensuring that they receive the support they need to thrive academically. This approach can foster a more positive and engaging learning experience, which can improve performance and motivation.

Additionally, educators can use a range of   educational intervention strategies   to offer individualized instruction. For example, they may use assessments to identify areas where students need help and use that information to create customized lesson plans. This approach can help students overcome learning obstacles and build the necessary skills and knowledge to succeed in their academic pursuits.

Another approach is to provide differentiated learning opportunities that cater to different learning styles and preferences. This strategy can ensure that struggling learners receive the support and resources they need to succeed.

Ultimately, individualized instruction and personalized learning plans are powerful   interventions for struggling learners . By providing targeted support that caters to each student's unique needs, educators can empower students to achieve academic success.

Small Group and Peer Tutoring

Small group and peer tutoring are effective   academic intervention programs   that can provide struggling students with additional support and guidance to enhance their learning experience. These interventions create a supportive environment that fosters collaboration, participation, and skill development. Unlike one-on-one tutoring, small group and peer tutoring provide students with opportunities for social interaction and feedback that can enhance their learning experience.

Benefits of Small Group and Peer Tutoring

Small group and peer tutoring interventions have several benefits for struggling students, such as:

• Opportunities for social interaction and feedback from peers and tutors
• Enhanced understanding of concepts through group discussions and collaborative activities
• Increased engagement and participation in the learning process
• Improved confidence and self-esteem
• Personalized and targeted support to address specific academic needs

These benefits make small group and peer tutoring effective   student intervention techniques   for addressing the academic challenges faced by struggling learners.

Implementing Small Group and Peer Tutoring

When implementing small group and peer tutoring interventions, it is essential to consider the following strategies:

• Grouping students based on their academic needs and strengths
• Providing clear learning objectives and goals for the sessions
• Creating engaging and interactive activities that promote collaboration and discussion
• Encouraging active participation and feedback from all students
• Assessing and evaluating the effectiveness of the intervention program regularly

When following these strategies, educators and support personnel can create meaningful and impactful small group and peer tutoring interventions that support struggling students in their academic journey.

Assistive Technology and Learning Resources

When it comes to supporting struggling learners, assistive technology and learning resources can make a significant difference. By providing access to tools and resources tailored to their needs, students can enhance their learning experience and improve their academic performance. Here are some effective   academic support services   and   educational intervention strategies :

Text-to-Speech Software

For students who struggle with reading and processing text, text-to-speech software can be an invaluable tool. This software allows students to hear written text read aloud, making it easier for them to understand and retain information. 

Graphic Organizers

Visual aids such as graphic organizers can help struggling students better organize and understand complex information. These tools can help students identify main ideas, make connections between ideas, and retain information more effectively. Popular graphic organizers include mind maps, flowcharts, and concept maps.

Online Learning Platforms

With the rise of online learning, various platforms have emerged, offering a range of resources and tools to support struggling learners. Platforms such as Khan Academy, Quizlet, and Duolingo provide interactive lessons, practice exercises, and personalized learning plans to students of all ages.

Adaptive Learning Software

Adaptive learning software is an innovative approach that uses technology to tailor the learning experience to each student's level and pace. By incorporating assistive technology and other learning resources into   academic interventions , educators can empower struggling learners and maximize their chances of success. Remember, every student's needs are unique, so it's essential to provide a variety of support services and strategies to ensure all students have the opportunity to thrive.

Social-Emotional Support and Counseling Services

Academic struggles can often stem from social-emotional challenges that students face. To support these students, it's crucial to provide access to social-emotional support and counseling services.

Student support strategies   such as mentorship programs, individual or group counseling, and creating a positive school climate can go a long way in helping struggling learners overcome emotional barriers that impede their academic progress.

Academic support services   such as academic advisors, success coaches, and supportive learning communities can also provide a sense of belonging and help build resilience in students facing academic challenges.

Benefits of Social-Emotional Support and Counseling Services

By providing social emotional support and counseling services, students can:

• Develop self-awareness and self-regulation skills
• Build healthy relationships with peers and adults
• Learn effective communication and conflict-resolution strategies
• Reduce stress and anxiety levels
• Develop coping skills and improve emotional well-being

Integrating Social-Emotional Support and Counseling Services into Academic Programs

Integrating social emotional support and counseling services into academic programs can be done in a variety of ways:

By integrating social emotional support and counseling services into academic programs, educators and support personnel can create a holistic approach to supporting struggling learners.

Remember, every student is unique, and there is no one-size-fits-all solution to academic difficulties. By tailoring interventions to the specific needs of each student, you can create a supportive learning environment that maximizes their chances of success.

Whether it's individualized instruction, small group tutoring, assistive technology, or social emotional support , there are a variety of interventions available to support struggling learners. By leveraging these interventions and providing students with the necessary resources and support, you can help them achieve academic success.

Author: Melody Reyes

Posted: 03 Nov 2023

Estimated time to read: 7 mins

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Module 2: Goal Setting and Time Management

Dealing with setbacks and obstacles, learning outcomes.

• Describe strategies to redefine or overcome challenges to achieving goals

Your priorities and goals may shift throughout your time in school. At times, unexpected events and challenges can get in the way of best-laid plans. For example, you might get sick or injured or need to deal with a family issue or a financial crisis, which might cause you to have to take time away from school. Or perhaps you may realize that you need to change your major, which would require a reevaluation of your goals and possibly spending more time in school. We can’t predict the future, and while goal setting is an important part of life, we also need to understand when we should reevaluate.

When Your Goals Get Sidetracked

Goals can become sidetracked . Other events in your life might distract you from achieving your immediate goals, even if they are goals that are really important to you and that you’ve worked hard to achieve. Consider the following scenario in which a student is challenged to reexamine her goals, priorities, and timetables:

Janine had thought she would be an accountant, even though she knew little about what an accounting job might entail. Her math and organizational skills were strong, and she enjoyed taking economics courses as well as other courses in her accounting program. But when one of her courses required her to spend time in an accounting office working with taxes, she decided that accounting was not the right fit for her due to the higher-stress environment and the late hours. At first she was concerned that she invested time and money in a career path that did not match her disposition. She feared that changing her major would add to her graduation time. Nevertheless, she did decide to change her major and her career focus. Janine is now a statistician with a regional healthcare system. She is very happy with her work. Changing her major from accounting to statistics was the right decision for her.

Janine could have stuck with her original goal to become an accountant, despite realizing that the job wasn’t a good fit for her. Instead, she didn’t allow the fear of changing her major or adding additional time in school prevent her from changing course once she knew being an accountant wasn’t right for her. Let’s take a closer look at how Janine may have worked through reprioritizing her goals and timeline.

Janine’s choice

Janine’s new information:  She didn’t enjoy the actual work of being an accountant.

Possible paths forward for Janine

• Pros: No more additional tuition money or time in school than she originally planned
• Cons: A life in a career she already knows she won’t enjoy
• Pros: The possibility to follow a career path she enjoys
• Cons: Potentially more student loan debt and more time spent in school
• Pros: She won’t have to decide what she wants to major in now that she’s decided she doesn’t want to major in accounting.
• Cons: She won’t have her college degree and may be no closer to understanding what career path she’s interested in pursuing.
• Pros: She’ll potentially have more time to think about this big life decision and try out some jobs or internships that would give her a better idea of other fields of work she might be interested in.
• Cons: It might be hard to get back into school after taking a break; she may not have a plan for how she’s going to support herself during her break from school.

Janine’s choice and the result:  Janine decided to change her major once she realized that she didn’t like accounting and that she wasn’t going to be able to apply her accounting major to a career outside of accounting. She ended up in a career that she’s happy with and realized that changing her major was the right move for her.

This scenario represents some of the many opportunities we have on an ongoing basis to assess our relationship to our goals, reevaluate priorities, and adjust. These opportunities exist every day—every moment, really! Remember, you can’t predict the outcome of every choice that you make, but if you take the time to really evaluate the options that are available to you instead of continuing on a path that isn’t working, or giving up all together, you never know what options and opportunities may come your way.

Problem-Solving Strategies

When you’re facing an unexpected event that forces you to change your approach or your goals, there are some helpful questions to consider. Below is a simple list of five problem-solving strategies. They can be applied to any aspect of your life.

1. Define the Problem

What is the problem? Define it in detail. How is it affecting you and other people? What is the cause of the problem? Why is this relevant to your life and the people in your life?

2. How Are Other People Dealing with This Problem?

Are there other people who have dealt with this same issue or issues like it? How are other people dealing with this problem? You might find information about this issue either from the people you know in real life or from articles on the Internet, podcasts, books, or other media. Ask yourself,

• are others making changes to their own behavior, such as adjusting their time management skills?
• can they still complete their responsibilities on time?
• are they accessing outside support and strategies to help them navigate this situation?

3. What Solutions Are Available to Me?

After examining how other people have dealt with this issue, you might want to come up with some of your own solutions (whether or not you incorporate aspects of other peoples’ approaches). It can help to make a list of possible solutions with pros and cons (the benefits of each choice and the drawback of each choice). Write down every solution you can think of; no option is too strange or impossible at this stage. You just want to get all your ideas down on paper so you can evaluate them later. Ask yourself,

• what is my range of possible solutions?
• how might these solutions help me reach my goal/s?

4. What Needs to Be Done?

This is the part where you evaluate whether or not your solutions are realistic. Ask yourself,

• what do I need to do to implement each possible solution?
• is it possible to do those things?
• what would it cost me, in money, time, effort, and other ways?

Make sure to think about how each solution might affect the other areas in your life, and what the pros and cons are for your current and future self.

5. Talk It Through with Someone You Trust

Once you have a list of possible solutions, you will probably know deep down which solution you’re most interested in pursuing first. If it helps, talk it through with a trusted friend (this could be a peer, a parent, or anybody in your life that you trust with making decisions). You might be surprised what perspective they bring to the situation that you hadn’t considered.

Change is Normal

No matter who you are, unexpected things are bound to happen that will disrupt your plans. Remembering this fact might help you feel more at ease when your plans get disrupted. Reevaluating and changing course is a normal part of life. Feel confident that you can return to your intended path in time, and remember that there can be learning opportunities even in the unexpected. Take some time to acknowledge the ways in which you need to regroup. If it helps, seek advice from people who have faced adversity. Line up your resources, be resolved, and proceed with certainty toward your goals.

If one advances confidently in the direction of his dreams, and endeavors to live the life which he has imagined, he will meet with a success unexpected in common hours. –Henry David Thoreau, author

Student Story: Burnout

This student story was written as part of Lumen’s College Success Student Contributors project. The story student stories are written in collaboration with real college students and college graduates to reflect real student experiences.

This year I returned to school for my postbacc to prepare myself for a master’s degree. I was working really hard in a department I hadn’t taken any classes in before. I had some background knowledge from my undergraduate degree in linguistics, but condensing all the communication disorders and sciences undergrad requirements into one academic year was really difficult.

I was really struggling to feel that I was successful. I viewed my grades as a direct reflection of my intellect, which I know isn’t true. Still, it really wasn’t easy to challenge that idea while I was in the thick of taking classes.

I found myself having to decide which classes I needed a minimal grade to pass, which is something I never had to do in my undergrad. On top of that, my partner was also in school pursuing a degree and I had a really hard time not comparing my academic performance to his.

While I was working on my postbacc to apply for my master’s degree, I felt a lot of imposter syndrome. I felt like I didn’t belong in school and wondered why I was even there trying to prepare myself for a master’s degree.

My career has been centered on working with children, but I know I get burnout. I was worried that I would hit a wall with my burnout and I wouldn’t be able to move past it to complete what I need to complete. In the past, I worked as a teacher’s aid for only two years and I burned out. To me, two years is not a long time.

I’ve changed a lot since then, but I’m still nervous about burnout. I’m worried about putting all this time and money into something like a master’s degree and then burning out on what I went to school for. I know it still might not be the right path for me, and that would be okay.

I had to take a reality check after my midterm when my burnout was making me apathetic about my grades. I saw the grade I received on my midterm and thought “I just don’t care.” I realized I shouldn’t think like that, but I had a hard time shaking the thought. I talked to my instructor and boss about it and I ended up joining a study group with some classmates.

I feel a lot more confident in my academic performance after sitting in that group study session. I know I wouldn’t have the motivation to study without somebody else there with me, but if somebody is depending on me to be present at the study session, then I can do that.

reevaluation: the process of honestly assessing the relationship between our values, goals, and sense of enjoyment, which may require us to change our action plan

sidetracked: getting derailed from our action plan, often as the result of an emergency or unexpected obstacle

• College Success. Authored by : Linda Bruce. Provided by : Lumen Learning. License : CC BY: Attribution
• Student Story: Burnout. Authored by : Anonymous. Provided by : Lumen Learning. License : CC BY: Attribution
• Image of detour. Authored by : Chris Dlugosz. Located at : https://flic.kr/p/5gSB8e . License : CC BY: Attribution
• Authored by : Kelly Sikkema. Provided by : Unsplash. Located at : https://unsplash.com/photos/xoU52jUVUXA . License : CC0: No Rights Reserved

Strategies for cultivating academic resilience

In a career that will make you question everything, challenge and failure are tools for moving forward, writes Andrew Stapleton. Here, he offers techniques to build resilience after setbacks

Andrew Stapleton

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Academic resilience, in my experience, is the capacity of students, researchers and educators to pursue their academic goals persistently in the face of challenges and setbacks.  It’s a simple idea, but continuing forward can be hard when things are going wrong. It involves maintaining motivation and commitment despite difficulties such as rigorous coursework, research obstacles or the pressure of academic competition. 

The most successful academics I’ve observed exhibit qualities that include:

• The adaptability to learn from failures and feedback, and continuously refine their approach
• The emotional strength to manage stress and anxiety
• The ability to build support systems, seek mentorship and leverage resources such as peer groups or advisers
• Problem-solving acumen, so they can navigate the complex academic environment with innovative solutions.

This article will help you use simple techniques to create academic resilience. 

Strategic techniques for enhancing resilience

Build a ‘success thesis’ to help you recover after setbacks.

In academia, where setbacks are as common as coffee in a faculty lounge, the secret weapon of the successful isn’t raw intelligence or hard work – it’s their systems. These systems act as a scaffold, supporting the core tenet of resilience, which is the ability to persist and continue forward. The approach involves structured tasks that you do every day, week or month and which will move you closer to your goals in research. 

• Click here for more resources for early career researchers
• Building resilience in students: give them roots and wings
• Book extract: ‘Resilience is grit amid uncertainty’

I often tell anyone who asks for my advice in developing their academic career to come up with a success thesis. This thesis typically will take this structure: “If I do [ these things ] over [ this amount of time ], it will be unreasonable for me to fail.” You can use this success thesis to craft systems that focus on the actions you need to perform and will get you to where to want to be. 

Such systems aren’t only about bouncing back; they’re also about embedding resilience and adaptability into your daily routine. Habits will make it natural to continue when things get tough.  A 2002 study found that participants experienced less stress when involved in habitual tasks.

This is a juicy detail that textbooks don’t teach: resilience is a system, a daily practice. It is as essential to an academic as their research topic. In academia, where uncertainty is the only certainty, a well-crafted system is the best companion you can have on your journey.

Realise that the best scientists have a long list of failures

Understanding that setbacks are experienced by accomplished academics can be a powerful tool for framing our own academic challenges.

Look to Princeton professor Johannes Haushofer, who published a “ CV of failures ” , for inspiration. His list of academic rejections demonstrates that failure is not only common but also an integral part of the academic process . This is particularly important for high achievers in their undergraduate years who might not be accustomed to frequent failure. 

This perspective is also crucial in research, where the likelihood that you’ll face rejection and setbacks is high.  Recognising that even the most successful figures in academia have navigated numerous failures can help reframe our approach to setbacks, seeing them not as insurmountable obstacles but as essential steps in the journey of learning and growth.

See challenges as necessary for excellence

In the realm of academic excellence, challenges are crucial. The wisdom in stoic teaching emphasises the importance of excellence. Seneca’s profound statement that “without a challenge, excellence withers” illustrates the role of adversity in fostering growth and excellence. 

This idea is particularly relevant in academia, where challenges in research, critical thinking and problem-solving are fundamental to intellectual advancement. Facing and overcoming these obstacles not only enhances resilience but also deepens understanding and expertise.  For example, being challenged by seemingly rude reviewers and collaborators almost always helps make a study or idea more robust. Also, failure in any research project forces us to rethink, deepen our knowledge and reformulate our plan.

It’s through navigating these challenges that scholars and students alike refine their skills, expand their knowledge and achieve true excellence in their fields. 

Intrinsic motivation works best when things go wrong

Recently, I was reading Drive by Daniel H. Pink , which illuminates the significance of intrinsic motivation, a concept he refers to as Motivation 3.0. Pink, who writes about business, work, creativity and behaviour, argues that intrinsically motivated individuals thrive when they have autonomy over their work, embodying self-direction and a deep engagement in their tasks. 

This approach contrasts with traditional extrinsic motivators such as monetary incentives. Pink’s theory is exemplified in projects such as Wikipedia, where volunteers driven by passion and interest contribute significantly without financial rewards. 

For academic researchers, this translates into fostering environments that emphasise autonomy, mastery and purpose. Look for opportunities in your work to explore topics you are genuinely passionate about and that allow flexible working methods; regularly connecting with the real-world impact of your work can also lead to more profound engagement and innovative outcomes.

Resilience is a continuous journey

Academic resilience is more than just a trait; it’s also a cultivated skill vital for success in academia. Understanding that even the most successful academics have faced numerous failures and that challenges are essential for excellence can reframe our perspectives on setbacks. 

Academic resilience is about persistently moving forward in the face of change and challenge, leveraging our failures as much as our successes and continuously striving for excellence through learning and growth. So embrace these principles, craft your success thesis and thrive.

Andrew Stapleton is a former researcher and founder of  Academia Insider . His YouTube channel focuses on academic AI tools, PhD advice and productivity.

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Problem-Solving Strategies and Obstacles

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From deciding what to eat for dinner to considering whether it's the right time to buy a house, problem-solving is a large part of our daily lives. Learn some of the problem-solving strategies that exist and how to use them in real life, along with ways to overcome obstacles that are making it harder to resolve the issues you face.

What Is Problem-Solving?

In cognitive psychology , the term 'problem-solving' refers to the mental process that people go through to discover, analyze, and solve problems.

A problem exists when there is a goal that we want to achieve but the process by which we will achieve it is not obvious to us. Put another way, there is something that we want to occur in our life, yet we are not immediately certain how to make it happen.

Maybe you want a better relationship with your spouse or another family member but you're not sure how to improve it. Or you want to start a business but are unsure what steps to take. Problem-solving helps you figure out how to achieve these desires.

The problem-solving process involves:

• Discovery of the problem
• Deciding to tackle the issue
• Seeking to understand the problem more fully
• Researching available options or solutions
• Taking action to resolve the issue

Before problem-solving can occur, it is important to first understand the exact nature of the problem itself. If your understanding of the issue is faulty, your attempts to resolve it will also be incorrect or flawed.

Problem-Solving Mental Processes

Several mental processes are at work during problem-solving. Among them are:

• Perceptually recognizing the problem
• Representing the problem in memory
• Considering relevant information that applies to the problem
• Identifying different aspects of the problem
• Labeling and describing the problem

Problem-Solving Strategies

There are many ways to go about solving a problem. Some of these strategies might be used on their own, or you may decide to employ multiple approaches when working to figure out and fix a problem.

An algorithm is a step-by-step procedure that, by following certain "rules" produces a solution. Algorithms are commonly used in mathematics to solve division or multiplication problems. But they can be used in other fields as well.

In psychology, algorithms can be used to help identify individuals with a greater risk of mental health issues. For instance, research suggests that certain algorithms might help us recognize children with an elevated risk of suicide or self-harm.

One benefit of algorithms is that they guarantee an accurate answer. However, they aren't always the best approach to problem-solving, in part because detecting patterns can be incredibly time-consuming.

There are also concerns when machine learning is involved—also known as artificial intelligence (AI)—such as whether they can accurately predict human behaviors.

Heuristics are shortcut strategies that people can use to solve a problem at hand. These "rule of thumb" approaches allow you to simplify complex problems, reducing the total number of possible solutions to a more manageable set.

If you find yourself sitting in a traffic jam, for example, you may quickly consider other routes, taking one to get moving once again. When shopping for a new car, you might think back to a prior experience when negotiating got you a lower price, then employ the same tactics.

While heuristics may be helpful when facing smaller issues, major decisions shouldn't necessarily be made using a shortcut approach. Heuristics also don't guarantee an effective solution, such as when trying to drive around a traffic jam only to find yourself on an equally crowded route.

Trial and Error

A trial-and-error approach to problem-solving involves trying a number of potential solutions to a particular issue, then ruling out those that do not work. If you're not sure whether to buy a shirt in blue or green, for instance, you may try on each before deciding which one to purchase.

This can be a good strategy to use if you have a limited number of solutions available. But if there are many different choices available, narrowing down the possible options using another problem-solving technique can be helpful before attempting trial and error.

In some cases, the solution to a problem can appear as a sudden insight. You are facing an issue in a relationship or your career when, out of nowhere, the solution appears in your mind and you know exactly what to do.

Insight can occur when the problem in front of you is similar to an issue that you've dealt with in the past. Although, you may not recognize what is occurring since the underlying mental processes that lead to insight often happen outside of conscious awareness .

Research indicates that insight is most likely to occur during times when you are alone—such as when going on a walk by yourself, when you're in the shower, or when lying in bed after waking up.

How to Apply Problem-Solving Strategies in Real Life

If you're facing a problem, you can implement one or more of these strategies to find a potential solution. Here's how to use them in real life:

• Create a flow chart . If you have time, you can take advantage of the algorithm approach to problem-solving by sitting down and making a flow chart of each potential solution, its consequences, and what happens next.
• Recall your past experiences . When a problem needs to be solved fairly quickly, heuristics may be a better approach. Think back to when you faced a similar issue, then use your knowledge and experience to choose the best option possible.
• Start trying potential solutions . If your options are limited, start trying them one by one to see which solution is best for achieving your desired goal. If a particular solution doesn't work, move on to the next.
• Take some time alone . Since insight is often achieved when you're alone, carve out time to be by yourself for a while. The answer to your problem may come to you, seemingly out of the blue, if you spend some time away from others.

Obstacles to Problem-Solving

Problem-solving is not a flawless process as there are a number of obstacles that can interfere with our ability to solve a problem quickly and efficiently. These obstacles include:

• Assumptions: When dealing with a problem, people can make assumptions about the constraints and obstacles that prevent certain solutions. Thus, they may not even try some potential options.
• Functional fixedness : This term refers to the tendency to view problems only in their customary manner. Functional fixedness prevents people from fully seeing all of the different options that might be available to find a solution.
• Irrelevant or misleading information: When trying to solve a problem, it's important to distinguish between information that is relevant to the issue and irrelevant data that can lead to faulty solutions. The more complex the problem, the easier it is to focus on misleading or irrelevant information.
• Mental set: A mental set is a tendency to only use solutions that have worked in the past rather than looking for alternative ideas. A mental set can work as a heuristic, making it a useful problem-solving tool. However, mental sets can also lead to inflexibility, making it more difficult to find effective solutions.

How to Improve Your Problem-Solving Skills

In the end, if your goal is to become a better problem-solver, it's helpful to remember that this is a process. Thus, if you want to improve your problem-solving skills, following these steps can help lead you to your solution:

• Recognize that a problem exists . If you are facing a problem, there are generally signs. For instance, if you have a mental illness , you may experience excessive fear or sadness, mood changes, and changes in sleeping or eating habits. Recognizing these signs can help you realize that an issue exists.
• Decide to solve the problem . Make a conscious decision to solve the issue at hand. Commit to yourself that you will go through the steps necessary to find a solution.
• Seek to fully understand the issue . Analyze the problem you face, looking at it from all sides. If your problem is relationship-related, for instance, ask yourself how the other person may be interpreting the issue. You might also consider how your actions might be contributing to the situation.
• Research potential options . Using the problem-solving strategies mentioned, research potential solutions. Make a list of options, then consider each one individually. What are some pros and cons of taking the available routes? What would you need to do to make them happen?
• Take action . Select the best solution possible and take action. Action is one of the steps required for change . So, go through the motions needed to resolve the issue.
• Try another option, if needed . If the solution you chose didn't work, don't give up. Either go through the problem-solving process again or simply try another option.

You can find a way to solve your problems as long as you keep working toward this goal—even if the best solution is simply to let go because no other good solution exists.

Sarathy V. Real world problem-solving .  Front Hum Neurosci . 2018;12:261. doi:10.3389/fnhum.2018.00261

Dunbar K. Problem solving . A Companion to Cognitive Science . 2017. doi:10.1002/9781405164535.ch20

Stewart SL, Celebre A, Hirdes JP, Poss JW. Risk of suicide and self-harm in kids: The development of an algorithm to identify high-risk individuals within the children's mental health system . Child Psychiat Human Develop . 2020;51:913-924. doi:10.1007/s10578-020-00968-9

Rosenbusch H, Soldner F, Evans AM, Zeelenberg M. Supervised machine learning methods in psychology: A practical introduction with annotated R code . Soc Personal Psychol Compass . 2021;15(2):e12579. doi:10.1111/spc3.12579

Mishra S. Decision-making under risk: Integrating perspectives from biology, economics, and psychology . Personal Soc Psychol Rev . 2014;18(3):280-307. doi:10.1177/1088868314530517

Csikszentmihalyi M, Sawyer K. Creative insight: The social dimension of a solitary moment . In: The Systems Model of Creativity . 2015:73-98. doi:10.1007/978-94-017-9085-7_7

Chrysikou EG, Motyka K, Nigro C, Yang SI, Thompson-Schill SL. Functional fixedness in creative thinking tasks depends on stimulus modality .  Psychol Aesthet Creat Arts . 2016;10(4):425‐435. doi:10.1037/aca0000050

Huang F, Tang S, Hu Z. Unconditional perseveration of the short-term mental set in chunk decomposition .  Front Psychol . 2018;9:2568. doi:10.3389/fpsyg.2018.02568

National Alliance on Mental Illness. Warning signs and symptoms .

Mayer RE. Thinking, problem solving, cognition, 2nd ed .

Schooler JW, Ohlsson S, Brooks K. Thoughts beyond words: When language overshadows insight. J Experiment Psychol: General . 1993;122:166-183. doi:10.1037/0096-3445.2.166

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Academic Goals: What Are They and How To Achieve Them

What are academic goals.

Academic Goals, also referred to as educational goals, are specific objectives or targets that a student sets for their academic performance and progress. These goals may vary depending on your personal interests, academic level, and career aspirations. Some common examples of academic goals include:

• Achieving a specific grade point average (GPA) or academic standing
• Completing a particular course or program with excellence
• Developing specific crucial skills, such as writing, research, or critical thinking
• Pursuing higher education, such as a college degree or professional certification
• Obtaining academic scholarships or awards
• Conducting original research or contributing to academic publications
• Building a professional network of mentors, peers, and colleagues in their field.

Educational goals help students stay focused and motivated in their studies and provide a roadmap for achieving success in their academic and professional careers.

Importance of Setting Academic Goals

Setting educational goals is an essential step toward achieving academic success. By setting clear and achievable goals, students can stay focused and motivated, and have a sense of direction throughout their academic journey. Academic goals help students to prioritize their time and efforts and allocate resources effectively, making it easier to achieve their desired outcomes. Moreover, setting these goals helps students to build self-confidence and self-efficacy, as they take ownership of their learning and progress toward their aspirations. Whether short-term or long-term, setting goals for your education is a powerful tool for academic achievement and personal growth .

Types of Academic Goals

There are different types of academic goals that students can set for themselves. Short-term goals are focused on immediate or near-term objectives, such as earning a particular grade in a course or completing a specific project. Medium-term goals are those that are typically set for a semester or academic year, such as improving overall GPA or completing a certain number of credit hours. Long-term goals are those that are often associated with career aspirations, such as pursuing a specific field of study or earning a particular degree.

Role of Academic Goals in Career Development 

The role of academic goals in career development and advancement is significant and multifaceted. Academic goals serve as a roadmap for individuals to acquire the knowledge, skills, and qualifications necessary to pursue their desired career paths. Here’s a detailed exploration of their role:

Clarity of Direction

Academic goals help individuals clarify their career aspirations by identifying the educational milestones necessary to enter their desired fields. Whether it’s obtaining a specific degree, certification, or specialized training, setting academic goals provides clarity and direction.

Skill Development

Academic pursuits provide opportunities for individuals to develop critical skills relevant to their chosen careers. Whether it’s technical expertise, problem-solving abilities, communication skills, or leadership qualities, achieving academic goals equips individuals with the competencies needed to excel in their professional endeavors.

Credentialing and Qualifications

Many careers require specific educational qualifications or certifications for entry and advancement. Academic goals serve as stepping stones towards obtaining these credentials, enhancing individuals’ credibility and competitiveness in the job market.

Professional Networking

Pursuing academic goals often involves interacting with peers, professors, and professionals in the field. These interactions facilitate networking opportunities, which can be instrumental in career development. Building relationships with mentors, industry professionals, and fellow students can lead to valuable insights, referrals, and career opportunities.

Career Advancement Opportunities

Academic achievements can open doors to advancement within one’s chosen career. Whether it’s earning advanced degrees, specialized certifications, or additional qualifications, meeting academic goals demonstrates commitment, ambition, and a willingness to invest in one’s professional growth, making individuals more attractive candidates for promotions and leadership roles.

Adaptability and Lifelong Learning

Setting and pursuing academic goals foster a mindset of adaptability and lifelong learning, essential qualities for success in today’s dynamic job market. As industries evolve and new technologies emerge, individuals with a commitment to ongoing education are better equipped to adapt to change, stay relevant, and seize new opportunities for career advancement.

Academic goals play a crucial role in shaping individuals’ career trajectories by providing direction, skill development, credentialing, networking opportunities, and pathways to advancement. By setting and pursuing academic goals, individuals can enhance their employability, expand their professional opportunities, and achieve greater fulfillment in their chosen careers.

How to Set Achievable Educational Goals

To set achievable educational goals, it is important to follow a few key steps. First, students should start by assessing their strengths, weaknesses, and interests, as this will help them to identify areas where they need to improve and where they are already strong. Next, they should identify their priorities and values, as this will help them to set goals that align with their overall career goals and aspirations. Once they have identified their strengths, weaknesses, interests, priorities, and values, students can then start to set specific, measurable, achievable, relevant, and time-bound goals, also known as SMART goals . Finally, students should regularly review their progress toward their academic goals and make adjustments as needed to ensure that they stay on track.

Strategies for Achieving Your Educational Goals

Academic success is a top priority for students, and setting clear and achievable goals is an essential step toward achieving it. Whether you’re aiming for high grades, academic recognition, or pursuing further education, developing effective strategies can help you stay focused and motivated throughout your academic journey. The following strategies can help you achieve your academic goals and excel in your studies. From effective time management to building strong study habits, we will provide you with practical tips and tools to help you succeed academically.

Time Management Techniques

Effective time management is critical for academic success. Students can use various time management techniques, such as creating a daily or weekly schedule, prioritizing tasks, breaking down larger assignments into smaller, manageable tasks, and setting deadlines. Time management techniques can help students to use their time more efficiently, reduce stress, and achieve their academic goals.

Study Strategies

Effective study strategies are essential for academic success. Students can use various study strategies, such as active reading, note-taking, summarizing, memorization techniques, and practice tests. By using effective study strategies, students can deepen their understanding of the material, retain information better, and perform better on exams.

Staying Motivated

Staying motivated is crucial for academic success. Students can use various techniques to stay motivated, such as setting clear and achievable goals, finding a sense of purpose in their studies, celebrating small achievements, and rewarding themselves for reaching milestones. Additionally, having a positive mindset, practicing self-care, and surrounding themselves with supportive peers and mentors can help students to stay motivated throughout their academic journey.

Getting Support From Mentors and Peers

Getting support from mentors and peers can help students to stay on track toward their academic goals. Mentors can offer guidance, share their experiences and expertise, and provide valuable feedback. Peers can provide a sense of community, offer support and encouragement, and help students to stay accountable. By building a strong support network, students can overcome challenges and achieve their educational goals.

How Do You Align Career Goals with Academic Goals?

Aligning career goals with academic goals involves creating a strategic plan to ensure that the educational pursuits undertaken are directly relevant to one’s desired career path. Here are steps to achieve alignment:

Clarify Career Objectives

Begin by clearly defining your career goals. Identify your desired industry, job role, and long-term aspirations within your chosen field. Understanding where you want to go professionally will guide the selection of academic pursuits that align with those objectives.

Research Academic Requirements

Research the educational requirements and qualifications typically sought after in your desired career. This may involve looking at job postings, industry standards, or consulting with professionals already working in the field. Determine the level of education, specific degrees, certifications, or specialized training commonly required or preferred.

Set Academic Milestones

Based on the educational requirements identified, set specific academic goals and milestones. This could include earning a certain degree, completing relevant coursework or training programs, obtaining certifications, or gaining hands-on experience through internships or research opportunities. Break down these goals into smaller, manageable tasks to track progress effectively.

Choose Relevant Courses and Programs

Select academic programs, courses, or workshops that directly contribute to your career goals. Look for courses that offer practical skills, industry-specific knowledge, or opportunities for experiential learning relevant to your desired field. Consider factors such as curriculum content, faculty expertise, accreditation, and networking opportunities when evaluating educational options.

Seek Mentorship and Guidance

Seek guidance from mentors, career counselors, or professionals working in your desired field. They can provide valuable insights, advice, and recommendations based on their own experiences. Engage in informational interviews, networking events, or professional organizations to expand your understanding of the industry and gain perspective on aligning academic and career goals.

Balance Breadth and Depth

While it’s essential to focus on acquiring specialized knowledge and skills relevant to your career goals, also consider the importance of a well-rounded education. Balance depth of expertise in your chosen field with a breadth of knowledge across complementary disciplines. Develop transferable skills such as critical thinking, communication, and adaptability that are valued across various industries.

Stay Flexible and Adapt

Remain open to adjusting your academic goals as you gain new insights, experiences, or opportunities. Industries evolve, and career paths may shift over time. Be willing to pivot, explore new interests, or pursue additional education or training that aligns with emerging trends or changing market demands.

Evaluate and Reflect

Regularly assess your progress towards aligning academic and career goals. Reflect on your achievements, challenges, and areas for growth. Evaluate the relevance and effectiveness of your academic pursuits in relation to your career objectives. Make adjustments as needed to stay on course and continue advancing towards your professional aspirations.

By following these steps, individuals can effectively align their academic goals with their career goals, ensuring that their educational pursuits directly contribute to their long-term success and fulfillment in their chosen professions.

Overcoming Obstacles to Achieving Your Educational Goals

Achieving academic goals can be challenging, and obstacles can arise that can hinder your progress. These obstacles can range from personal issues such as lack of motivation or time management skills to external factors such as financial constraints or a difficult academic environment. However, with the right mindset and approach, these obstacles can be overcome, and your academic goals can be achieved. In this article, we will discuss some of the common obstacles that students face and provide you with effective strategies to overcome them. Whether you are struggling with a difficult course, time management, or personal issues, we will help you identify the problem and provide you with practical solutions to help you succeed.

Common Obstacles to Achieving Academic Goals

Several common obstacles can hinder students’ academic performance and progress. These include:

• Procrastination : Procrastination is a common issue that affects many students. It can lead to missed deadlines, poor grades, and increased stress levels.
• Time management : Poor time management skills can lead to a lack of productivity and poor academic performance.
• Lack of motivation : Students may lose motivation due to a lack of interest in the subject, personal issues, or academic challenges.
• Financial constraints : Financial difficulties can make it challenging for students to afford academic resources and pursue higher education.

Developing resilience

Resilience is the ability to overcome challenges and bounce back from setbacks. Developing resilience is essential for achieving educational goals and overcoming obstacles. Here are some ways to develop resilience:

• Set realistic goals : Setting realistic goals can help students avoid becoming overwhelmed and maintain motivation.
• Practice self-care : Self-care activities such as exercise, meditation, and socializing can help students manage stress and maintain a positive mindset.
• Seek support : Students can seek support from family, friends, academic advisors, or mental health professionals to cope with challenges and overcome obstacles.

Strategies for overcoming obstacles

Here are some strategies for overcoming common obstacles to achieving academic goals:

• Create a study schedule : Creating a study schedule can help students manage their time effectively and avoid procrastination.
• Break tasks into manageable chunks : Breaking large tasks into smaller, more manageable ones can help students avoid feeling overwhelmed and stay motivated.
• Find motivation : Students can find motivation by setting personal goals, seeking out inspiring role models, or participating in extracurricular activities.
• Use available resources : Students can use academic resources such as tutors, study groups, and online resources to improve their academic performance.

Achieving the goals you set for your education requires resilience, dedication, and the ability to overcome obstacles. By identifying common obstacles, developing resilience, and using effective strategies, students can overcome challenges and achieve their academic goals. 

Celebrating Success and Revising Goals in College

Celebrating success and revising goals are crucial steps toward academic achievement in college. While reaching academic milestones is a major accomplishment, taking time to celebrate these achievements can provide students with motivation and confidence to strive toward their future academic goals. Additionally, regularly revising goals can help students ensure they are on the right path to achieving their aspirations. 

College is an opportunity for students to explore and discover their interests and passions, and revising their goals can help them align their academic pursuits with their personal and professional aspirations. By celebrating successes and revising goals, college students can stay motivated and focused on achieving academic success.

Recognizing achievements

Celebrating success is an important part of achieving academic goals. When students accomplish their objectives, it is essential to take a moment to recognize and appreciate their achievements. Celebrating success can come in many forms, such as getting together with friends, family, or mentors, attending an awards ceremony or reception, or even taking some time to reflect on personal accomplishments. By acknowledging and celebrating success, students can build confidence and motivation, inspiring them to strive for even greater achievements in the future.

Re-evaluating and Revising Goal

Revising goals is a natural part of the academic process. As students grow and change, their goals may shift, requiring a re-evaluation of their academic objectives. It is essential to regularly assess and revise goals to ensure that they remain relevant and meaningful. Re-evaluating goals may involve reviewing progress, examining personal interests and passions, seeking feedback from peers or mentors, or considering new challenges or opportunities. By reassessing goals, students can ensure that they are staying on track and making progress toward achieving their academic aspirations.

Achieving Your Academic Goals

Achieving your academic goals requires commitment, hard work, and a clear plan of action. By following the steps outlined in this article, including setting realistic and specific goals, creating a schedule and study plan, seeking out resources and support, celebrating success, and re-evaluating goals regularly, you can make steady progress toward your academic aspirations. Remember, achieving academic success is a journey that requires patience and perseverance, but with dedication and effort, you can accomplish your goals and realize your full potential. Best of luck on your academic journey!

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Academic difficulties are a common challenge that many students face during their educational journey. These challenges can stem from a variety of sources, such as learning disabilities, personal issues, or a lack of effective study strategies. Helping students cope with academic difficulties is essential for their success and well-being. 

Educators, parents, and counselors play a crucial role in providing the support and resources needed to overcome these obstacles. This article explores several effective ways to help students navigate academic challenges and thrive in their studies.

Leveraging Counseling Services

Counseling services can play a pivotal role in helping students cope with academic difficulties. Professional counselors are trained to provide emotional and psychological support, which can be particularly beneficial for students dealing with stress, anxiety, or other personal issues affecting their academic performance.

Counselors can work with students to develop coping strategies for managing stress and improving mental health. This might include techniques such as mindfulness, relaxation exercises, or cognitive-behavioral strategies to challenge negative thought patterns. By addressing mental health issues, counselors can help students build resilience and improve their overall well-being.

They can also assist students in developing academic skills. This might involve teaching effective study techniques, goal-setting, and time-management strategies. By helping students build these essential skills, counselors can empower them to take control of their learning and achieve their academic goals.

Counselors who have completed online counseling degrees are often more attuned to the nuances of today's academic challenges. Their unique educational backgrounds, which combine traditional learning with digital tools, enable them to effectively help students manage and overcome academic difficulties.

Providing Individualized Support

One of the most effective ways to help students cope with academic difficulties is to provide individualized support. Every student is unique, and their challenges and needs will vary. Tailoring support to address these specific needs can significantly enhance their learning experience and outcomes.

First, it’s essential to identify the root cause of the academic difficulties. This may involve assessing the student’s learning style, identifying any learning disabilities, or understanding personal or environmental factors affecting their performance. Once the underlying issues are identified, a personalized plan can be developed to address these challenges.

For example, students with learning disabilities may benefit from specialized instruction techniques, such as multi-sensory learning or the use of assistive technology. Those struggling with time management might need help developing effective organizational skills and creating a structured study schedule. Providing resources like tutoring, mentoring, or study groups can also offer additional support and help students stay on track.

Communication between educators, parents, and students is crucial in this process. Regular meetings and progress reviews can help monitor the student’s development and make necessary adjustments to the support plan. By fostering a collaborative environment, all parties can work together to ensure the student receives the most effective assistance.

Encouraging Parental Involvement

Parental involvement is a crucial factor in helping students cope with academic difficulties. Parents play a significant role in their child’s education, and their support and involvement can greatly influence a student’s academic success and well-being.

One way parents can support their children is by fostering a positive learning environment at home. This includes providing a quiet and organized space for studying, setting a consistent routine, and encouraging good study habits. Parents should also be proactive in communicating with teachers and staying informed about their child’s progress and any challenges they may be facing.

In addition to creating a supportive home environment, parents can help their children develop important skills such as time management, organization, and problem-solving. These skills are essential for academic success and can help students navigate their studies more effectively.

Moreover, parents should encourage their children to pursue their interests and strengths. This can boost their motivation and engagement in school. Supporting extracurricular activities, hobbies, and interests outside of academics can also provide a well-rounded experience and help students manage stress and maintain a healthy balance.

It's also important for parents to be aware of their child’s emotional and mental well-being. Regular check-ins and open conversations about their feelings and experiences can help parents identify any issues early and seek appropriate support. Being a source of emotional support and understanding can make a significant difference in a student's ability to cope with academic difficulties.

Helping students cope with academic difficulties requires a multifaceted approach that includes individualized support, counseling services, and parental involvement. By identifying and addressing the unique challenges each student faces, providing access to professional counseling, and fostering a supportive home and school environment, educators and parents can significantly enhance students' ability to overcome academic obstacles. 

These efforts not only improve academic performance but also contribute to students' overall well-being and development. By working together, we can create a nurturing and empowering educational experience that helps every student succeed

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Funded, in part, through a Cooperative Agreement with the U.S. Small Business Administration. All opinions, and/or recommendations expressed herein are those of the author(s) and do not necessarily reflect the views of the SBA.

10 Strategies and Practices That Can Help All Students Overcome Barriers

2. be intentional with your lesson planning, 3. use a balanced data approach, 4. have high and consistent expectations, 5. scaffold instruction to grade level standards, 6. teach vocabulary explicitly, 7. get your students engaged and excited, 8. reflect and reflect often, 9. provide multiple opportunities; strive to embed learning, 10. don’t be afraid to be vulnerable.

1. Build relationships

Rachael George is principal of Sandy Grade School-Oregon Trail School District.

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Ways to Overcome Academic Challenges

School life can often be a whirlwind of lessons, projects, and exams. While this academic journey is enriching, students may encounter challenges along the way. These academic challenges can range from time management issues to difficulties in understanding new concepts, and dealing with them effectively is crucial for educational success. At Chatsworth International School, we believe in empowering students with strategies to overcome these challenges and thrive academically.

Common Academic Challenges

Understanding the nature of these challenges is the first step towards finding effective solutions. Let's delve into the specifics of a few common academic difficulties that students often grapple with:

Time management

In a world that seems to move at lightning speed, balancing schoolwork, cocurricular activities, personal time, and sometimes even part-time jobs can be overwhelming. Learning how to manage time to study effectively is a valuable skill that goes beyond the classroom. It helps students to make the most of their day, complete tasks on time, and reduce stress.

Difficulty understanding new concepts

As students advance in their academic journey, they are introduced to increasingly complex subjects and ideas. This progress is a natural part of learning, but sometimes students might find it hard to grasp these new concepts. It's important to remember that this difficulty is not a reflection of their intelligence or ability, but often simply a sign that they may need to approach the concept from a different angle or spend some extra time digesting the new information.

Lack of motivation

Schoolwork can occasionally feel monotonous and overwhelming, leading to a lack of motivation. Motivation is the fuel that keeps the flame of curiosity and interest alive. In such situations, understanding the underlying cause of this lack of motivation can help address it. Is it a lack of understanding, a perceived lack of relevance, or simply burnout? Each cause requires a unique solution.

Language barriers

For students in an international school environment, language barriers can sometimes pose a challenge. This is particularly true for those who are non-native English speakers. Overcoming these barriers takes time, patience, and practice but can lead to rich rewards in communication skills and cultural understanding.

Strategies to Overcome These Challenges

Facing academic challenges can seem daunting, but remember, every challenge presents an opportunity for growth and learning. Here are some strategies to help students navigate these academic hurdles:

Developing effective study habits

In the quest to maximise learning and retain information, implementing techniques like active recall, spacing out the study, taking some time to explain the concepts you have learned to your parents, siblings or friends and studying in widely varying contexts can significantly enhance comprehension and boost learning. These techniques can help transform studying from a passive to an active process, making study time more productive and less stressful.

Time management goes beyond just keeping track of time. It involves organising tasks, prioritising work based on importance and deadlines, and utilising tools like calendars, planners, and to-do lists. Effective time management reduces stress, boosts productivity, and enhances overall academic performance.

Seeking help from teachers or tutors

There's no shame in asking for help when students are struggling with a specific subject or concept. Teachers or tutors are equipped with the knowledge and teaching strategies to provide personalised guidance and support. Their expertise can help students gain a fresh perspective and overcome academic hurdles.

Collaboration with peers

Collaborative learning through study groups can provide a platform for students to learn from each other, clear doubts, and gain new perspectives. It not only reinforces understanding but also cultivates teamwork, a vital skill in today's interconnected world.

Setting realistic goals

Setting achievable academic goals can help students maintain motivation and provide a clear direction. These goals act as stepping stones to larger aspirations and help students monitor their progress, enhancing their sense of achievement and confidence.

Maintaining a growth mindset

Having a growth mindset encourages students to view challenges as opportunities for learning rather than obstacles. This mindset fosters resilience and perseverance, essential traits that equip students to tackle future challenges with confidence and positivity.

Nurturing Success at Chatsworth International School

What can you do if your child is struggling at school? For parents, it's crucial to remember that academic struggles do not define your child's intelligence or their potential. These challenges are opportunities for your child to develop critical life skills like problem-solving, time management, and perseverance. More than just academic support, your understanding, patience, and encouragement can make a world of difference in helping your child navigate these difficulties.

As one of the best international schools in Singapore , our mission goes beyond academic excellence. We are committed to fostering an environment that not only supports students academically but also nurtures their well-being. Our teachers are more than educators – they are mentors who understand individual student needs and provide personalised support, guidance, and encouragement. Our collaborative study spaces encourage peer learning, where students can share knowledge, exchange ideas, and gain new perspectives. We believe in empowering students with effective learning strategies and a growth mindset, helping them see challenges as opportunities for growth rather than setbacks.

Remember, academic challenges are just a part of the larger educational journey. With the right strategies, support, and mindset, every student has the potential to overcome these obstacles, grow from them, and emerge stronger. At Chatsworth International School , we are here to support this journey, empowering every student to unlock their full academic potential and become lifelong learners.

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3 Ways to Overcome Obstacles and Achieve Your Goals

Research examines why some people encounter fewer obstacles during goal pursuit..

Posted August 30, 2022 | Reviewed by Abigail Fagan

• What Is Motivation?
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• Sometimes people pursue goals that reflect their interests (“want-to motivation”) and sometimes only because they should (“have-to motivation”).
• Have-to motivation is related to preference for settings with obstacles more numerous or placed closer to the person.
• Want-to motivation and self-control are related to preference for settings with obstacles less numerous or placed farther away.

Why is it, have you ever wondered, that certain goals are very difficult to achieve? Is it because you don’t want to pursue these goals but have to ? And is that challenging because you lack discipline and self-control ?

Or does the answer have to do with your perception of obstacles—seeing obstacles as acute, numerous, insurmountable, and so on?

A recent paper provides some answers. Let me first introduce a few concepts before I discuss the paper, which was written by Leduc-Cummings and collaborators and published in the August issue of Journal of Research in Personality .

Want-to motivation, have-to motivation, and self-control

Sometimes people pursue a goal that reflects their interests, preferences, desires, and values. This means they have want-to motivation . For instance, a person very interested in getting fit will likely have high want-to motivation—to exercise, learn about exercise routines, diets, supplements, etc.

Other times, people pursue a goal because of pressures and expectations. For example, if a person has no interest in getting fit but is told she must lose weight for health reasons, then she has greater have-to motivation than want-to motivation for working out.

Another concept related to goal pursuit is self-control .

Self-control refers to the capacity to bring one’s responses in line with valued goals. Self-control is often associated with self-discipline, willpower, and the ability to remain patient and reject short-term pleasures for long-term benefits.

An important question is whether people high in self-control are good at resisting temptation or if they experience fewer temptations in the first place.

Previous research indicates that many people who are successful at self-control engage in effortless self-regulation , which means they encounter fewer temptations. Why?

Possibly because they select relatively obstacle-free environments or modify their setting to reduce distractions, temptations, or competing goals (e.g., turning off the phone while studying). Or because they perceive fewer difficulties in the first place. For instance, athletes may not see cold weather as a barrier to outdoor exercise.

These possibilities were tested in the experiments described below.

Investigating trait self-control, motivation, and obstacles

In seven investigations, Leduc-Cummings et al. explored “the relationship between motivation (want-to and have-to) and trait self-control, and the preferred (Studies 1-3a-b) or actual (Studies 4-6) positioning of obstacles in the environment .”

In Studies 3a-b and 4, they additionally examined “the relation between motivation (want-to and have-to), trait self-control, and the subjective perception of obstacles, and compare[d] it to the relation with the preferred positioning of obstacles.”

They used two types of samples (students and MTurk workers), two goal domains (school success, healthy eating), and three settings (real life, laboratory, Internet).

• Study 1: Participants were asked to imagine scenarios involving obstacles to healthy eating.
• Study 2: Similar to the previous investigation, but the goal was school success.
• Studies 3a and 3b: The scenarios once again involved eating healthy, in addition to subjective perception of obstacles.
• Study 4: This was a field study (library) on actual obstacles to school success.
• Studies 5 and 6: In-lab situations involving obstacles to the goal of eating healthy.

The results showed that “want-to motivation and trait self-control were related to people setting up their environments in such a way that reduces the experience of obstacles, while have-to motivation was associated with setting up obstacles closer to the self.”

For example, people who were forced to diet had placed, probably automatically and without conscious intent, the bags of chips and cookies on the lower shelves in the pantry, which means these unhealthy snacks were within reach.

Furthermore, “have-to motivation was associated with perceiving obstacles as being more problematic, whereas trait self-control was associated with perceiving obstacles as less problematic.”

Finally, “when comparing the effects of motivation on preferences for positioning the obstacles and on subjective perception, results showed that the relation with perceptions were stronger for have-to motivation, but not different for want-to motivation.”

Enhancing or sabotaging our efforts to reach a goal

As important as willpower is for successful goal pursuit, research findings show that our environment is important too.

The research reviewed concluded that self-control and want-to motivation (pursuing a goal that is interesting or desirable) are associated with an inclination toward choosing environments with a smaller number of obstacles and with putting more distance between oneself and obstacles.

For instance, if you really want to eat healthy, you are less likely to walk by candy stores or vending machines on the way home from work. And, at home, you place healthier foods closer and unhealthy foods, like candies or ice cream, out of the way.

In contrast, have-to motivation (internal pressures, external demands, “shoulds”) is associated with a predilection toward environments with a larger number of obstacles or with putting less distance between oneself and obstacles.

To illustrate, if you are reading a challenging book only because you have to (reading it for a mandatory class), you may unconsciously have the TV on in the background or a favorite comic book nearby.

Needless to say, in the long run, having mixed feelings and ambivalence toward a goal can have negative consequences, leading to more self-sabotage and even disengagement from the goal.

We all struggle with self-motivation, especially for certain tasks. (This post offers strategies on how to motivate yourself to do chores you hate doing .)

But effective self-control is not so much about resisting temptations as it is about reducing the likelihood or the experience of temptation. How? This may involve setting better and more authentic goals , developing healthy habits , and using situational and perceptual strategies discussed today. In summary, you can use three techniques to achieve your goal:

• Situation selection strategies . Examples: working out at the gym instead of at home; avoiding the candy aisle in the grocery store.
• Situation modification strategies . Examples: using smaller plates to eat less; putting on noise-canceling headphones when studying in a noisy environment.
• Perception modification strategies . These include techniques involving attentional deployment, cognitive reappraisal, and mindfulness . To illustrate, here is a mindfulness technique to reduce chocolate cravings .

Arash Emamzadeh attended the University of British Columbia in Canada, where he studied genetics and psychology. He has also done graduate work in clinical psychology and neuropsychology in U.S.

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The six main barriers against problem-solving and how to overcome them.

Challenges. Disputes. Dilemmas. Obstacles. Troubles. Issues. Headaches.

• The uniqueness of every different issue makes the need for an also adapted and individualized solution.
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Business people discussing the plan at office photo credit: Getty

There are more than thirty different ways to call all those unpleasant and stressful situations which prevent us from directly achieving what we want to achieve. Life is full of them. This is why the ability to solve problems in an effective and timely manner without any impediments is considered to be one of the most key and critical skill for resolutive and successful leaders. But is not just leaders or top managers facing the way forward.  According to a Harvard Bussiness Review survey , people's skills depends on their level on the organization and their particular job and activities. However, when coming to problem-solving, there is a remarkable consistency about the importance of it within all the different measured organization levels.

There are small problems and big problems. Those ones that we laugh about and those that take our sleep away. Problems that affect just us or our whole company. Issues that need to be resolved proactively and others that require us to wait and observe.  There is a special kind of problem for every day of our lives, but all of them responds to a common denominator: addressing them adequately.  It is our ability to do so what makes the difference between success and failure.

Problems manifest themselves in many different ways. As inconsistent results or performance. As a failure toward standards.  As discrepancies between expectations and reality.  The uniqueness of every different issue makes the need for an also adapted and individualized solution. This is why finding the way forward can be sometimes tricky. There are many reasons why it is difficult to find a solution to a problem, but you can find the six more common causes and the way to overcome them!

1. Difficulty to recognize that there is a problem

Nobody likes to be wrong. “Cognitive dissonance is what we feel when the self-concept — I’m smart, I’m kind, I’m convinced this belief is true — is threatened by evidence that we did something that wasn’t smart, that we did something that hurt another person, that the belief isn’t true,” explains Carol Tavris. 

Problems and mistakes are not easy to digest. To  reduce this cognitive dissonance, we need to modify our self-concept or well deny the evidence. Many times is just easier to simply turn our back to an issue and blindly keep going.  But the only way to end it up to satisfactory is to make an effort to recognize and accept the evidence. Being wrong is human and until the problem is not acknowledged solutions will never materialize. To fully accept that something is not going the way it should, the easiest way is to focus on the benefits of new approaches and always remain non-judgemental about the causes. Sometimes we may be are afraid of the costs in terms of resources, time and physical or mental efforts that working for the solution may eventually bring. We may need then to project ourselves in all the fatalistic consequences that we will finally encounter in case we continue sunk in the problem. Sometimes we really need to visualize the disaster before accepting a need for change.

2. Huge size problem

Yes! We clearly know that something is going wrong. But the issue is so big that there is no way we can try to solve it without blowing our life into pieces. Fair enough. Some problems are so big that it is not possible to find at once a solution for them. But we can always break them into smaller pieces and visualize the different steps and actions that we could eventually undertake to get to our final goal. Make sure you do not lose sight of the original problem!

3. Poorly framed problem

Without the proper framing, there is no certainty about the appropriate focus on the right problem. Asking the relevant questions is a crucial aspect to it. Does your frame of the problem capture its real essence? Do you have all the background information needed? Can you rephrase the problem and it is still understandable? Have you explored it from different perspectives? Are different people able to understand your frame for the problem correctly? Answering to the right problem in the right way depends 95% on the correct framing of it!

'If I have an hour to solve a problem, I'd spend 55 minutes thinking about the problem and 5 minutes thinking about the solution' (Albert Einstein)

4. Lack of respect for rhythms

There is always a right time for preparation, a right time for action and a right time for patience. Respecting the rhythms of a problem is directly link to the success of the solution. Acting too quickly or waiting too long can have real counterproductive effects. There is a need for enough time to gather information and understand all the different upshots of a planned solution. A balance of action is crucial to avoid both eagerness and laxity. Waiting for the proper time to take action is sometimes the most complicated part of it.

5. Lack of problem'roots identification

It is quite often that we feel something is not going the way it should without clearly identifying what the exact problematic issue is. We are able to frame all the negative effects and consequences, but we do not really get to appropriately verbalized what the problem is all together. Consequently, we tend to fix the symptoms without getting to the real causes.  It is as common as dangerous and not sustainable for problem-solving. 

Make sure that you have a clear picture of what are the roots of the problem and what are just the manifestations or ramifications of it. Double loop always to make sure that you are not patching over the symptoms but getting to the heart of the matter.

6. Failure to identify the involved parts

Take time to figure out and consult every simple part involved in the problem as well as affected by the possible solution. Problems and solutions always have at the core human needs and impacts. Failing to identify and take into consideration the human factor in the problem-solving process will prevent the whole mechanism from reaching the desired final goal.

'We always hope for the easy fix: the one simple change that will erase a problem in a stroke. But few things in life work this way. Instead, success requires making a hundred small steps go right - one after the other, no slipups, no goofs, everyone pitching in.'  ( Atul Gawande)

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10 Problem-solving strategies to turn challenges on their head

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What is an example of problem-solving?

What are the 5 steps to problem-solving, 10 effective problem-solving strategies, what skills do efficient problem solvers have, how to improve your problem-solving skills.

Problems come in all shapes and sizes — from workplace conflict to budget cuts.

Creative problem-solving is one of the most in-demand skills in all roles and industries. It can boost an organization’s human capital and give it a competitive edge. 

Problem-solving strategies are ways of approaching problems that can help you look beyond the obvious answers and find the best solution to your problem . 

Let’s take a look at a five-step problem-solving process and how to combine it with proven problem-solving strategies. This will give you the tools and skills to solve even your most complex problems.

Good problem-solving is an essential part of the decision-making process . To see what a problem-solving process might look like in real life, let’s take a common problem for SaaS brands — decreasing customer churn rates.

To solve this problem, the company must first identify it. In this case, the problem is that the churn rate is too high. 

Next, they need to identify the root causes of the problem. This could be anything from their customer service experience to their email marketing campaigns. If there are several problems, they will need a separate problem-solving process for each one. 

Let’s say the problem is with email marketing — they’re not nurturing existing customers. Now that they’ve identified the problem, they can start using problem-solving strategies to look for solutions. 

This might look like coming up with special offers, discounts, or bonuses for existing customers. They need to find ways to remind them to use their products and services while providing added value. This will encourage customers to keep paying their monthly subscriptions.

They might also want to add incentives, such as access to a premium service at no extra cost after 12 months of membership. They could publish blog posts that help their customers solve common problems and share them as an email newsletter.

The company should set targets and a time frame in which to achieve them. This will allow leaders to measure progress and identify which actions yield the best results.

Perhaps you’ve got a problem you need to tackle. Or maybe you want to be prepared the next time one arises. Either way, it’s a good idea to get familiar with the five steps of problem-solving. 

Use this step-by-step problem-solving method with the strategies in the following section to find possible solutions to your problem.

1. Identify the problem

The first step is to know which problem you need to solve. Then, you need to find the root cause of the problem. 

The best course of action is to gather as much data as possible, speak to the people involved, and separate facts from opinions. 

Once this is done, formulate a statement that describes the problem. Use rational persuasion to make sure your team agrees .

2. Break the problem down 

Identifying the problem allows you to see which steps need to be taken to solve it. 

First, break the problem down into achievable blocks. Then, use strategic planning to set a time frame in which to solve the problem and establish a timeline for the completion of each stage.

3. Generate potential solutions

At this stage, the aim isn’t to evaluate possible solutions but to generate as many ideas as possible. 

Encourage your team to use creative thinking and be patient — the best solution may not be the first or most obvious one.

Use one or more of the different strategies in the following section to help come up with solutions — the more creative, the better.

4. Evaluate the possible solutions

Once you’ve generated potential solutions, narrow them down to a shortlist. Then, evaluate the options on your shortlist. 

There are usually many factors to consider. So when evaluating a solution, ask yourself the following questions:

• Will my team be on board with the proposition?
• Does the solution align with organizational goals ?
• Is the solution likely to achieve the desired outcomes?
• Is the solution realistic and possible with current resources and constraints?
• Will the solution solve the problem without causing additional unintended problems?

5. Implement and monitor the solutions

Once you’ve identified your solution and got buy-in from your team, it’s time to implement it. 

But the work doesn’t stop there. You need to monitor your solution to see whether it actually solves your problem. 

Request regular feedback from the team members involved and have a monitoring and evaluation plan in place to measure progress.

If the solution doesn’t achieve your desired results, start this step-by-step process again.

There are many different ways to approach problem-solving. Each is suitable for different types of problems. 

The most appropriate problem-solving techniques will depend on your specific problem. You may need to experiment with several strategies before you find a workable solution.

Here are 10 effective problem-solving strategies for you to try:

• Use a solution that worked before
• Brainstorming
• Work backward
• Use the Kipling method
• Draw the problem
• Use trial and error
• Sleep on it
• Get advice from your peers
• Use the Pareto principle
• Add successful solutions to your toolkit

Let’s break each of these down.

1. Use a solution that worked before

It might seem obvious, but if you’ve faced similar problems in the past, look back to what worked then. See if any of the solutions could apply to your current situation and, if so, replicate them.

2. Brainstorming

The more people you enlist to help solve the problem, the more potential solutions you can come up with.

Use different brainstorming techniques to workshop potential solutions with your team. They’ll likely bring something you haven’t thought of to the table.

3. Work backward

Working backward is a way to reverse engineer your problem. Imagine your problem has been solved, and make that the starting point.

Then, retrace your steps back to where you are now. This can help you see which course of action may be most effective.

4. Use the Kipling method

This is a method that poses six questions based on Rudyard Kipling’s poem, “ I Keep Six Honest Serving Men .” 

• What is the problem?
• Why is the problem important?
• When did the problem arise, and when does it need to be solved?
• How did the problem happen?
• Where is the problem occurring?
• Who does the problem affect?

Answering these questions can help you identify possible solutions.

5. Draw the problem

Sometimes it can be difficult to visualize all the components and moving parts of a problem and its solution. Drawing a diagram can help.

This technique is particularly helpful for solving process-related problems. For example, a product development team might want to decrease the time they take to fix bugs and create new iterations. Drawing the processes involved can help you see where improvements can be made.

6. Use trial-and-error

A trial-and-error approach can be useful when you have several possible solutions and want to test them to see which one works best.

7. Sleep on it

Finding the best solution to a problem is a process. Remember to take breaks and get enough rest . Sometimes, a walk around the block can bring inspiration, but you should sleep on it if possible.

A good night’s sleep helps us find creative solutions to problems. This is because when you sleep, your brain sorts through the day’s events and stores them as memories. This enables you to process your ideas at a subconscious level. 

If possible, give yourself a few days to develop and analyze possible solutions. You may find you have greater clarity after sleeping on it. Your mind will also be fresh, so you’ll be able to make better decisions.

8. Get advice from your peers

Getting input from a group of people can help you find solutions you may not have thought of on your own. 

For solo entrepreneurs or freelancers, this might look like hiring a coach or mentor or joining a mastermind group. 

For leaders , it might be consulting other members of the leadership team or working with a business coach .

It’s important to recognize you might not have all the skills, experience, or knowledge necessary to find a solution alone. 

9. Use the Pareto principle

The Pareto principle — also known as the 80/20 rule — can help you identify possible root causes and potential solutions for your problems.

Although it’s not a mathematical law, it’s a principle found throughout many aspects of business and life. For example, 20% of the sales reps in a company might close 80% of the sales. 

You may be able to narrow down the causes of your problem by applying the Pareto principle. This can also help you identify the most appropriate solutions.

10. Add successful solutions to your toolkit

Every situation is different, and the same solutions might not always work. But by keeping a record of successful problem-solving strategies, you can build up a solutions toolkit. 

These solutions may be applicable to future problems. Even if not, they may save you some of the time and work needed to come up with a new solution.

Improving problem-solving skills is essential for professional development — both yours and your team’s. Here are some of the key skills of effective problem solvers:

• Critical thinking and analytical skills
• Communication skills , including active listening
• Decision-making
• Planning and prioritization
• Emotional intelligence , including empathy and emotional regulation
• Time management
• Data analysis
• Research skills
• Project management

And they see problems as opportunities. Everyone is born with problem-solving skills. But accessing these abilities depends on how we view problems. Effective problem-solvers see problems as opportunities to learn and improve.

Ready to work on your problem-solving abilities? Get started with these seven tips.

1. Build your problem-solving skills

One of the best ways to improve your problem-solving skills is to learn from experts. Consider enrolling in organizational training , shadowing a mentor , or working with a coach .

2. Practice

Practice using your new problem-solving skills by applying them to smaller problems you might encounter in your daily life. 

Alternatively, imagine problematic scenarios that might arise at work and use problem-solving strategies to find hypothetical solutions.

3. Don’t try to find a solution right away

Often, the first solution you think of to solve a problem isn’t the most appropriate or effective.

Instead of thinking on the spot, give yourself time and use one or more of the problem-solving strategies above to activate your creative thinking. 

4. Ask for feedback

Receiving feedback is always important for learning and growth. Your perception of your problem-solving skills may be different from that of your colleagues. They can provide insights that help you improve. 

5. Learn new approaches and methodologies

There are entire books written about problem-solving methodologies if you want to take a deep dive into the subject. 

We recommend starting with “ Fixed — How to Perfect the Fine Art of Problem Solving ” by Amy E. Herman. 

6. Experiment

Tried-and-tested problem-solving techniques can be useful. However, they don’t teach you how to innovate and develop your own problem-solving approaches. 

Sometimes, an unconventional approach can lead to the development of a brilliant new idea or strategy. So don’t be afraid to suggest your most “out there” ideas.

7. Analyze the success of your competitors

Do you have competitors who have already solved the problem you’re facing? Look at what they did, and work backward to solve your own problem. 

For example, Netflix started in the 1990s as a DVD mail-rental company. Its main competitor at the time was Blockbuster. 

But when streaming became the norm in the early 2000s, both companies faced a crisis. Netflix innovated, unveiling its streaming service in 2007. 

If Blockbuster had followed Netflix’s example, it might have survived. Instead, it declared bankruptcy in 2010.

Use problem-solving strategies to uplevel your business

When facing a problem, it’s worth taking the time to find the right solution. 

Otherwise, we risk either running away from our problems or headlong into solutions. When we do this, we might miss out on other, better options.

Use the problem-solving strategies outlined above to find innovative solutions to your business’ most perplexing problems.

If you’re ready to take problem-solving to the next level, request a demo with BetterUp . Our expert coaches specialize in helping teams develop and implement strategies that work.

Boost your productivity

Maximize your time and productivity with strategies from our expert coaches.

Elizabeth Perry, ACC

Elizabeth Perry is a Coach Community Manager at BetterUp. She uses strategic engagement strategies to cultivate a learning community across a global network of Coaches through in-person and virtual experiences, technology-enabled platforms, and strategic coaching industry partnerships. With over 3 years of coaching experience and a certification in transformative leadership and life coaching from Sofia University, Elizabeth leverages transpersonal psychology expertise to help coaches and clients gain awareness of their behavioral and thought patterns, discover their purpose and passions, and elevate their potential. She is a lifelong student of psychology, personal growth, and human potential as well as an ICF-certified ACC transpersonal life and leadership Coach.

8 creative solutions to your most challenging problems

5 problem-solving questions to prepare you for your next interview, what are metacognitive skills examples in everyday life, what is lateral thinking 7 techniques to encourage creative ideas, 31 examples of problem solving performance review phrases, learn what process mapping is and how to create one (+ examples), leadership activities that encourage employee engagement, how much do distractions cost 8 effects of lack of focus, can dreams help you solve problems 6 ways to try, the pareto principle: how the 80/20 rule can help you do more with less, thinking outside the box: 8 ways to become a creative problem solver, 3 problem statement examples and steps to write your own, contingency planning: 4 steps to prepare for the unexpected, when you need to set the direction, swot analysis is a classic tool, stay connected with betterup, get our newsletter, event invites, plus product insights and research..

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6 Common Problem Solving Barriers and How Can Managers Beat them?

What is the meaning of barriers to problem solving, what are the 6 barriers to problem solving, examples of barriers to problem solving, how to overcome problem solving barriers at work tips for managers, problem solving barriers faqs.

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Lack of motivation

Lack of knowledge, lack of resources, emotional barriers, cultural and societal barriers, fear of failure.

• Lack of motivation: A person who lacks motivation may struggle to complete tasks on time or produce quality work. For example, an employee who is disengaged from their job may procrastinate on essential tasks or show up late to work.
• Lack of knowledge : Employees who lack knowledge or training may be unable to perform their duties effectively. For example, a new employee unfamiliar with the company’s software systems may struggle to complete tasks on their computer.
• Lack of resources: Employees may be unable to complete their work due to a lack of resources, such as equipment or technology. For example, a graphic designer who doesn’t have access to the latest design software may struggle to produce high-quality designs.
• Emotional barriers: Emotional barriers can affect an employee’s ability to perform their job effectively. For example, an employee dealing with a personal issue, such as a divorce, may have trouble focusing on their work and meeting deadlines.
• Cultural and societal barriers: Cultural and societal barriers can affect an employee’s ability to work effectively. For example, an employee from a different culture may struggle to communicate effectively with colleagues or may feel uncomfortable in a work environment that is not inclusive.
• Fear of failure : Employees who fear failure may avoid taking on new challenges or may not take risks that could benefit the company. For example, an employee afraid of making mistakes may not take on a leadership role or hesitate to make decisions that could impact the company’s bottom line.
• Identify and Define the Problem: Define the problem and understand its root cause. This will help you identify the obstacles that are preventing effective problem solving.
• C ollaborate and Communicate: Work with others to gather information, generate new ideas, and share perspectives. Effective communication can help overcome misunderstandings and promote creative problem solving.
• Use Creative Problem Solving Techniques: Consider using creative problem solving techniques such as brainstorming, mind mapping, or SWOT analysis to explore new ideas and generate innovative solutions.
• Embrace Flexibility: Be open to new ideas and approaches. Embracing flexibility can help you overcome fixed mindsets and encourage creativity in problem solving.
• Invest in Resources: Ensure that you have access to the necessary resources, such as time, money, or personnel, to effectively solve complex problems.
• Emphasize Continuous Learning: Encourage continuous learning and improvement by seeking feedback, evaluating outcomes, and reflecting on the problem solving process. This can help you identify improvement areas and promote a continuous improvement culture.

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Blockchain, artificial intelligence, and healthcare: the tripod of future—a narrative review

• Open access
• Published: 08 August 2024
• Volume 57 , article number  238 , ( 2024 )

Cite this article

You have full access to this open access article

• Archana Bathula 1 , 2 ,
• Suneet K. Gupta 3 ,
• Suresh Merugu 4 ,
• Luca Saba 5 ,
• Narendra N. Khanna 6 ,
• John R. Laird 7 ,
• Skandha S. Sanagala 8 ,
• Rajesh Singh 9 ,
• Deepak Garg 10 ,
• Mostafa M. Fouda 11 &
• Jasjit S. Suri 12 , 13 , 14 , 15 , 16  

The fusion of blockchain and artificial intelligence (AI) marks a paradigm shift in healthcare, addressing critical challenges in securing electronic health records (EHRs), ensuring data privacy, and facilitating secure data transmission. This study provides a comprehensive analysis of the adoption of blockchain and AI within healthcare, spotlighting their role in fortifying security and transparency leading the trajectory for a promising future in the realm of healthcare. Our study, employing the PRISMA model, scrutinized 402 relevant articles, employing a narrative analysis to explore the fusion of blockchain and AI in healthcare. The review includes the architecture of AI and blockchain, examines AI applications with and without blockchain integration, and elucidates the interdependency between AI and blockchain. The major findings include: (i) it protects data transfer, and digital records, and provides security; (ii) enhances EHR security and COVID-19 data transmission, thereby bolstering healthcare efficiency and reliability through precise assessment metrics; (iii) addresses challenges like data security, privacy, and decentralized computing, forming a robust tripod. The fusion of blockchain and AI revolutionize healthcare by securing EHRs, and enhancing privacy, and security. Private blockchain adoption reflects the sector’s commitment to data security, leading to improved efficiency and accessibility. This convergence promises enhanced disease identification, response, and overall healthcare efficacy, and addresses key sector challenges. Further exploration of advanced AI features integrated with blockchain promises to enhance outcomes, shaping the future of global healthcare delivery with guaranteed data security, privacy, and innovation.

Avoid common mistakes on your manuscript.

1 Introduction

Artificial intelligence (AI) is the most advanced and complex human creation since its theoretical emergence in the early 1950s (Duan et al. 2019 ). In today’s technology-driven society, data is the new gold. Massive volumes of data are available for research and development, propelling the AI revolution (Dinh and Thai 2018 ). A variety of AI techniques take an interdisciplinary approach and may be used in a variety of sectors, including generalized medicine (Saba et al. 2012 ; Hamet and Tremblay 2017 ; Straw 2020 ), medical diagnosis (Suri 2008 ; Saba et al. 2022 ), and healthcare (Jiang et al. 2017 ; Yu et al. 2018 ; Davenport and Kalakota 2019 ). AI techniques, including computer-aided diagnosis (CAD) systems, have the potential to transform medical diagnosis and enhance patient outcomes (Castellino 2005 ; Giger and Suzuki 2008 ). By combining the knowledge of healthcare professionals with AI algorithms, CAD enables more accurate and efficient disease detection (Doi et al. 1999 ; Baaske et al. 2022 ). CAD systems heavily rely on diverse and comprehensive datasets to learn patterns and features from large amounts of data, enabling precise diagnoses and predictions (Abbasian Ardakani et al. 2021 ; Baaske et al. 2022 ). The availability of high-quality datasets is crucial for ensuring the reliability and effectiveness of CAD systems (Bancilhon et al. 1985 ). AI is simpler to use and better at detecting and predicting sickness, but a lack of data restricts its usage (Kohli et al. 2017 ; Chen et al. 2018 ). Using AI, doctors can better diagnose and forecast illness (Suri and Rangayyan 2006 ; Kuppili et al. 2017 ; Tandel et al. 2020 ). Epidemic highlights need to understand how COVID-19 data is acquired, transferred, and reported to forecast and prevent the spreading of covariant (Ienca and Vayena 2020 ). AI-based systems (a) learn, (b) interpret, and (c) draw conclusions from enormous data sets (Bengio and LeCun 2007 ). Machine learning (ML) algorithms perform better with data from a trustworthy, legitimate, secure, and trusted platform or data repository (Gupta et al. 2020a , b ). In addition, paper-based medical records have been replaced with electronic ones, necessitating a secure method to transmit and gather data (Huang et al. 2019 ). Some major issues in healthcare are (a) data storage and security (b) healthcare system interoperability, (c) decentralizing digital healthcare, and (d) data transparency and trust (Churi et al. 2021 ). Since most healthcare data for AI model training comes from a closed ecosystem of siloed organizations, it may be inaccurate or biased. (Barhamgi and Bertino 2022 ). Key medical information stakeholders include doctors, researchers, healthcare organizations, and the government (Chen et al. 2019 ). To overcome these limitations, a fine balance between data protection, global health, public trust as well as robust human-AI interactions is essential (Naudé 2020 ). Best practices should be defined to ensure appropriate data collection and standards worldwide (Ienca and Vayena 2020 ). AI has the potential to revolutionize the healthcare industry if provided with secure data and transparency (Saba et al. 2019 ). Using encrypted and privacy-preserving technology, the Blockchain may help AI secure data and markets, decentralize computation, and coordinate untrusted devices. (Pandl et al. 2020 ).

The Blockchain is a distributed ledger that stores and exchanges data in a cryptographically safe, authenticated, and immutable way (Nofer et al. 2017 ). Blockchain is a connected network of blocks that preserves transactions permanently (Dinh et al. 2017 ; Zheng et al. 2017 ). This approach uses a document’s timestamp, which cannot be altered (King and Nadal 2012 ; Nofer et al. 2017 ). Previously, Blockchain was primarily employed as the underpinning technology for cryptocurrencies and Bitcoin (Nakamoto 2008 ; Narayanan et al. 2016 ). Blockchain has various application areas, ranging from supply chain (Francisco and Swanson 2018 ), education (Gräther et al. 2018 ; Bathula et al. 2022a , b ), edge-computing services (Xiong et al. 2018 ), internet-of-things (IoT) (Reyna et al. 2018 ), finance (Treleaven et al. 2017 ), and the sharing of clinical or radiological data (Zhang et al. 2018 ; Tagliafico et al. 2022 ). The use of Blockchain in healthcare is gaining popularity in academic and non-academic fields (Ekblaw et al. 2016 ) and may be used to regulate access to electronic health information (Dagher et al. 2018 ).

In addition to the challenges highlighted in the introduction, the healthcare industry faces significant hurdles in ensuring data storage, security, and interoperability (Churi et al. 2021 ). Traditional paper-based medical records have been replaced with electronic systems, necessitating secure methods for data transmission and storage (Weeks 2013 ). Moreover, the closed ecosystem of siloed healthcare organizations contributes to data fragmentation and bias, limiting the effectiveness of AI models trained on incomplete or biased datasets (Barhamgi and Bertino 2022 ). The fusion of AI and blockchain technologies in healthcare offers promising solutions to overcome these challenges. By leveraging the decentralized and immutable nature of blockchain technology, AI systems can access secure, tamper-proof data repositories, thereby mitigating concerns regarding data integrity and privacy (Pandl et al. 2020 ). Blockchain-based solutions offer promising avenues for regulating access to electronic health information, facilitating secure data exchange, and ensuring patient privacy (Dagher et al. 2018 ).

The proposed review attempts to study how the fusion of AI and blockchain technologies can address existing challenges in healthcare data privacy, and security. By analyzing current research and emerging trends, the review seeks to provide insights into best practices and strategies for harnessing the transformative potential of AI and blockchain in healthcare. Figure  1 demonstrates the intersection of Blockchain and AI in healthcare. Data from hospitals, patients, clinical labs, and diagnostic centers is collected (top region). This data fuels AI and Blockchain applications (“AI Layer” and “Blockchain Layer”) with a focus on security.

The convergence of Blockchain and AI in healthcare: a systematic integration for revolutionizing data management. API application programming interface, EHR electronic health records, IPFS interplanetary file system

The outcomes are utilized by physicians, healthcare providers, researchers, the public, and government agencies (bottom region). Integration is achieved through APIs (yellow boxes), while data storage is handled by the IPFS). When AI and Blockchain are combined, they may provide a secure, immutable, and decentralized system for sensitive data (Marwala and Xing 2018 ). Adopting Blockchain will entail AI’s interpretability, trust, and privacy issues. As a result, the integration of these two technologies seems inevitable (Dinh and Thai 2018 ; Zhang et al. 2021b ).

1.1 Motivation

The proposed review’s major motivation is to explore the potential benefits of integrating Blockchain and AI in healthcare, emphasizing the importance of privacy and trust across multiple institutions (Zheng et al. 2019 ; Zerka et al. 2020 ). With the growing adoption of digital solutions and the need for secure and efficient healthcare delivery, there is a pressing demand to explore innovative approaches that leverage AI and blockchain. The study aims to address this demand by examining the applications, issues, and solutions related to the fusion of AI and blockchain in healthcare. By identifying key trends and insights, the study aims to enlighten healthcare practitioners, researchers, and the government about the transformative potential of AI-driven healthcare solutions supported by blockchain technology. Ultimately, the motivation lies in advancing the understanding of how this fusion of emerging technologies can revolutionize healthcare delivery and improve patient outcomes.

1.2 Contributions

Understanding and presenting a detailed review of the fusion of AI and blockchain in healthcare.

Identifying trends, challenges, and potential benefits associated with this integration in healthcare.

Identifying AI’s potential hazards in the healthcare system.

Highlights fusion of AI and blockchain technologies in securing EHRs and addressing COVID-19 data transmission.

The study covers key aspects including Blockchain processes, features applied in healthcare, various Blockchain types, challenges, and their solutions, as well as the architecture of Blockchain-based healthcare systems.

Underscores the significance of AI and blockchain dependencies for decentralized computing and scalable architectures.

The study emphasizes blockchain’s potential to enhance data security, privacy, and transmission in healthcare AI, while also adding to the knowledge on how blockchain can benefit the AI-driven healthcare sector.

Highlights advanced AI techniques like pruning and explainable AI combined with Blockchain for enhancing healthcare systems for the future.

Here is the review’s structure and content: Sect.  2 details the literature search strategy using the PRISMA model, providing statistical insights into the prevalence of AI and Blockchain integration in healthcare research. Section  3 introduces Blockchain engineering basics, while Sect.  4 offers a concise overview of Artificial Intelligence. Section  5 explores the synergies between AI and Blockchain, emphasizing their interdependence. Section  6 delves into the integration of Blockchain with AI in healthcare, presenting a tripod framework for analysis. In Sect.  7 , a critical discussion examines challenges and opportunities, considering real-world applications, and Sect.  8 concludes the study.

2 Search strategy and its statistical distribution

This review is based on 402 research and review works. The section has been split into two segments: one discusses the citation selection employing the PRISMA model, while the other part analyzes the statistical distributions of the respective repositories.

2.1 PRISMA model

The PRISMA schema model was adapted for this study, and an extensive search was carried out on PubMed, IEEE, and Google scholar, resulting in the consideration of 402 relevant articles. Keywords used included “AI in healthcare,” “Blockchain and healthcare,” “deep learning in healthcare,” “federated learning and healthcare,” “Blockchain and deep learning,” “Blockchain and federated learning,” “Blockchain and deep reinforcement learning (DRL),” “AI and Blockchain integration,” “AI and Blockchain in healthcare supply chain management,” “AI and Blockchain in e-Health,” “AI and Blockchain in EHR,” “AI and Blockchain in clinical research and supply chain,” “AI without Blockchain,” “Blockchain without AI,” “AI integration with Blockchain,” and “Blockchain integration with AI”. In this study, we used bibliometric variables and the narrative literature review (NLR) approach (Jahan et al. 2016 ). We focused on comprehensive research articles with innovative techniques in healthcare, excluding irrelevant and non-analytical publications like conference abstracts, posters, and hypothetical discussions. The PRISMA model illustrating the incorporation of Blockchain and AI-related citations utilized in our study is displayed in Fig.  2 . Here we meticulously detail the procedures undertaken to choose and analyze the 402 studies. Here’s an overview of our Article Selection Process:

Search Strategy: A comprehensive search was conducted using PubMed

, IEEE, and Google Scholar databases to identify relevant articles.

Initial Search Results: A total of 9239 articles were sourced from PubMed, 6321 articles from IEEE, and 2,36,703 articles from Google Scholar, resulting in a combined total of 2,52,263 articles.

Screening Process: The titles and abstracts of the remaining articles were screened against the inclusion and exclusion criteria, resulting in the exclusion of 2,13,288 records.

Duplicate Removal: To locate duplicate articles, we utilized Clarivate Analytics' EndNote software “Find Duplicates” function (Analytics 2018 ) and removed 31,723 records.

Article Eligibility: Following the initial screening, 7252 articles were deemed eligible for full-text review.

Full-Text Review: The full texts of these articles were reviewed in detail to assess their relevance and alignment with the research objectives. A total of 4127 articles with full text were evaluated for eligibility.

Insufficient Data Exclusion: After the full-text review, a total of 3731 articles were excluded due to insufficient data or lack of relevance to the research topic.

Final Selection: Out of the evaluations conducted, articles that met the eligibility criteria were selected for further analysis, contributing to a total of 402 included articles.

PRISMA selection model

2.2 Statistical distributions and trends in AI-enabled blockchain articles in healthcare

2.2.1 blockchain and ai-enabled articles are considered for healthcare studies.

Out of 402 relevant articles considered for this research, there are a total of 79 publications that use both AI and Blockchain in healthcare, with a refined analysis centered on 51 implemented projects. The statistical distribution and discussion are based on these 51 publications. We can see from Fig.  3 a that there has been a massive rise in the number of articles produced, mostly appearing between 2020 and 2023. In 2017, only a single study combined AI and Blockchain in healthcare. In 2018, there were two studies published about merging AI and Blockchain in healthcare, while four in 2019. The number of publications published climbed substantially, to 20 in 2022 and 24 in 2023. Figure  3 b shows the number of articles implemented. Out of 79 articles on AI and Blockchain in healthcare, 51 are implemented, while 28 are not. Figure  3 c displays the distribution of implemented and unimplemented articles from 2017 to 2023 about the use of AI and Blockchain in healthcare. In summary, there has been an increase in the fusion of blockchain, and AI, in the healthcare industry.

a Distribution of Published Articles Using AI and Blockchain in Healthcare Research. b A Comparison of Implemented and Non-Implemented Articles on Blockchain and AI Integration in Healthcare. c Annual Distribution of AI and Blockchain Integration in Healthcare Implemented and Non-Implemented Articles (2017–2023)

2.2.2 Exploring the diverse landscape of AI-enabled blockchain articles: blockchain types

Examining the implemented integrated articles, blockchain types utilized by these healthcare articles are (i) public (ii), private (iii) consortium, and (iv) hybrid (Alhadhrami et al. 2017 ). Figure  4 a gives an illustration of several articles that employed different forms of Blockchains specific to the healthcare sector. Figure  4 b gives a more detailed analysis of year-wise publications utilizing the type of blockchain in implemented articles.

a Number of articles employing a type of blockchain in implemented articles. b Distribution of blockchain types employed in AI and Blockchain Integration in Healthcare articles (2017–2023)

Out of all the 51 implemented articles, 20 publications used private Blockchain, 14 used public Blockchain, only two employed consortium Blockchain, and one utilized hybrid Blockchain. Public blockchains offer users convenience through their open, permissionless access and decentralized structure, thereby enhancing simplicity and user autonomy. A private blockchain allows for limited access and data sharing among authorized users. The consortium blockchain has been in use since 2021, and its popularity is growing rapidly. Because of the complex nature of the hybrid blockchain, it has rarely been used by researchers. Despite the growing popularity of Blockchain, many authors in their articles do not specify the type of Blockchain used.

2.2.3 Exploring the diverse landscape of AI-enabled blockchain articles: utilised AI techniques

Examining the implemented integrated articles, AI techniques utilized by these healthcare articles are discussed here. Figure  5 a gives an illustration that employed different forms of AI techniques used in the 51 fusion articles specific to the healthcare industry. Figure  5 b gives a more detailed analysis of year-wise publications utilizing the type of AI technique in implemented articles. The analysis of articles utilizing various AI techniques across the years reveals an evolving landscape. In the cumulative span from 2017 to 2023, out of fifty-one published articles a total of seven articles incorporated Machine Learning (ML), with a noticeable rise from 2019 onwards. Deep Learning (DL) demonstrated consistent adoption, with a total of eleven articles, highlighting its sustained relevance. Federated Learning (FL) gained prominence, contributing to eight articles, particularly peaking in 2022. Reinforcement Learning (RL) and the combination of ML + DL each played a role in three articles. Remarkably, the combination of DL + FL emerged as a prevalent choice, appearing in thirteen articles. The application of Explainable-AI (XAI) techniques surfaced in one article, reflecting a growing interest in transparent AI methodologies.

a Number of articles employing AI techniques in implemented articles. b Distribution of AI techniques employed in AI and Blockchain Integration in Healthcare articles (2017–2023). AI Artificial Intelligence, ML Machine learning, DL Deep learning, FL Federated learning, RL Reinforcement learning, XAI :Explainable AI

2.2.4 Exploring the diverse landscape of AI-enabled blockchain articles: diverse applications

The merging of AI and blockchain technology has ushered in new avenues of exploration in healthcare. These advancements are being used in a spectrum of applications in the healthcare sector namely for early diagnosis, patient care, and data security. Figure  6 shows how Blockchain integration with AI is being used for various applications in healthcare from 2017 to 2023. Securing (EHR), electronic health data (EHD), and patients’ healthcare records (PHCR) as well as transmitting and updating COVID-19-related data securely among hospitals are the top two applications, accounting for 39% and 31% of all articles, respectively. The remaining 30% contribute to the ever-changing field of Blockchain and AI in healthcare by being employed in a variety of sectors such as clinical research, cardiovascular medicine, organ transplant systems, illness prediction, and specialized medical professions.

Various Applications with the fusion of AI and Blockchain in healthcare

Table 1 includes references to numerous research publications on the of AI and blockchain in healthcare, and the suggested review in 2023. It contains information such as the reference and year, application focus, keywords used, period covered, number of citations, and checkboxes indicating the existence of AI (ML, DL, FL, RL, XAI) and blockchain (BC) technology. Alzheimer’s disease, COVID-19, e-health, cancer care, public health, EHR management systems, and healthcare insurance are among the topics covered in the review papers. Our proposed review comprehensively covers various facets of healthcare and heavily emphasizes both AI and blockchain, demonstrating a holistic approach to the integration of both technologies.

3 The engineering of blockchain-background

3.1 the blockchain processes.

Blockchain is a technology that doesn't have to be controlled by anyone, and it only became popular after it was used for Bitcoin in 2008 (Narayanan et al. 2016 ; Nakamoto 2008 ). Blockchain’s key characteristics, such as anonymity, confidentiality, data integrity, and lack of the need for a third-party organization, are boosting interest in Blockchain (Feng et al. 2019 ; Islam et al. 2020 ). A Blockchain is a sequence of blocks that contain verifiable transaction data (Niranjanamurthy et al. 2019 ) that are linked together by a connection (hash value) to the preceding block, forming a chain-like structure (Zheng et al. 2017 ). The header of a block contains information about the date, a hash representation, the hash of the previous block, and the cryptographic nonce (Zheng et al. 2017 ). The first block of a blockchain is commonly known as the genesis block (Nakamoto 2008 ). The genesis block is represented with ‘0000’ in Fig.  7 as it does not have any previous hash. Data integrity and immutability are ensured by the transactions in each block and their references to the preceding block.

Blockchain block diagram

The fundamental architecture of Blockchain is seen in Fig.  8 , which consists of a connected chain of blocks. In each block, it has the attributes such as header, timestamp, nonce, data, and previous hash. Consensus methods are created to assess the trustworthiness of the blocks and decide which node will store the next block and how other nodes will confirm the newly added block. Some examples of consensus algorithms are Proof-of-work (PoW) (Nakamoto 2008 ), Proof-of-stake (PoS) (King and Nadal 2012 ), and Practical byzantine-fault tolerance (PBFT) (Castro and Liskov 1999 ; King and Nadal 2012 ). The Blockchain process steps specific to healthcare are explained in Fig.  9 . It has a sender, receiver, and a distributed database to do transactions and create a block. It uses cryptographic hashing for the distribution and validation process. The transactions are committed to the Blockchain once the validation is done by the nodes.

The basic blockchain structure

The blockchain process

3.2 Blockchain-based security, big data privacy and availability

Considering the findings of a study on data (Halevy et al. 2009 ), if given enough data, even the most basic AI system could surpass most modern technology. For the goal of disease diagnosis and detection, big data may be analyzed using AI algorithms. Such data is used to create the models, for example, machine learning since more relevant data may lead to generalized training leading to more accurate predictions. Here, the key challenge is to determine how to make data interchangeably reliable, secure, and available (Alhazmi et al. 2022 ). Genuine information integrity may be compromised for several reasons (Hernandez and Zhang 2017 ). The scale of fraudulent actions has grown alongside the development of technology. As a result, one of the most important aspects of the process is not having proper security procedures. Even during the outbreak, it was difficult to manage Coronavirus patient data due to databases all over the world as the data was massive, and was manipulated along the way (Kabir and Marlow 2022 ). Moreover, the European Medicines Agency (EMA) has detailed how hackers acquired official communications concerning COVID-19 vaccine evaluations. We are aware that a single, deidentified dataset is necessary for predictive analytics in healthcare to improve model training (Lee et al. 2017 ). In healthcare, it is exceptionally challenging to collect massive volumes of data, keep it secure, and make it accessible on demand without compromising security. Fortunately, Blockchain technologies may provide a feasible solution to this problem (Cheng et al. 2020 ; Huang et al. 2020 ; Archana Bathula 2022 ). Blockchain has emerged as a potentially game-changing technology with various features among which the two most crucial aspects of it are (i) data security and (ii) privacy (Zhang et al. 2021a , b ). The data structures created by Blockchain technology already have security safeguards built into them, which is a crucial component of the Blockchain’s security (Zheng et al. 2017 ).

Blockchain’s Distributed Ledger Technology (DLT) organizes data into blocks. In a cryptographic chain, each block is linked to the ones before it to prevent tampering, and hence obtain immutability (Zhang and Jacobsen 2018 ). A consensus mechanism and cryptography safeguard validate and approve each block transaction, ensuring its legitimacy and trustworthiness (Pan et al. 2021 ). The network’s nodes will serve as a repository for the data stored on the blockchain. It will be safe from riots, natural disasters, hacking attempts, and other threats that currently bring down and destroy centralized systems (Alhazmi et al. 2022 ) and from a single point of failure or any information manipulation or theft. In a distributed blockchain data storage network, a backup copy of the data is always accessible and may be easily retrieved whenever necessary, regardless of what happens to a node. Patients can set access limitations for their medical data using Blockchain, enabling researchers’ temporary access (Dagher et al. 2018 ; Liu et al. 2020a , b ). Blockchain’s Zero-Knowledge Proof (ZKP) (Sun et al. 2021 ), is a reliable method in such a scenario, wherein one node (the prover) demonstrates to another node (the verifier) that it is aware of a certain piece of information without disclosing the information’s actual content (Yang and Li 2020 ). With the use of ZKP private and secure transactions may be accomplished on a public Blockchain (Rasheed et al. 2021 ).

3.3 Types of blockchains

There are three kinds of DLT systems and they are permissioned, permissionless, and hybrid networks, and their characteristics are based on the user’s activities and data availability (Nofer et al. 2017 ; Niranjanamurthy et al. 2019 ). The specific aims and needs of the network influence the choice of a blockchain network. Enterprise environments that prioritize privacy and control are best suited to permissioned Blockchains. Permissionless Blockchains offer high transparency, security, and trust. Hybrid Blockchains combine permissioned and permissionless Blockchains. The varieties of blockchains with their accessing capabilities are shown in Fig.  10 .

Types of blockchains

Public blockchains , exemplified by Bitcoin and Ethereum, stand as open and permissionless networks accessible to anyone (Nakamoto 2008 ). Renowned for their attributes of transparency, decentralization, and immutability, these networks prove ideal for applications demanding trust and security without reliance on a central authority. Bitcoin functions on a Proof of Work (PoW) model, where miners competitively solve puzzles to validate transactions (Velde 2013 ). Ethereum 2.0 introduces a Proof of Stake (PoS) blockchain, selecting validators based on their cryptocurrency holdings (Dannen 2017 ). “Endless OS” (EOS) utilizes Delegated Proof of Stake (DPoS), enabling token holders to choose delegates for transaction validation (Hu et al. 2021a , b ). IOTA follows a unique approach with a Directed Acyclic Graph (DAG), where transactions confirm previous ones (Živić et al. 2020 ; Bathula et al. 2022a , b ).

Hybrid blockchains blend features from both permissioned and permissionless models, offering a flexible solution where parts can be public while others remain private (Marar and Marar 2020 ). This approach is ideal for scenarios requiring a balance between transparency and privacy. An extension of hybrid blockchains involves integrating side chains, offering scalability and efficiency by handling specific tasks independently (Back et al. 2014 ).

Private Blockchains are authorized networks with limited participation and access. They are often used by organizations or consortiums to maintain control over the network and ensure data privacy (Dinh et al. 2017 ). They provide selective data exchange among authorized parties, quicker transaction processing, and improved scalability.

Consortium blockchains blend aspects of private and public blockchains, maintained cooperatively by selected members. They offer a more decentralized approach than private blockchains, with consensus methods varying (Dib et al. 2018 ). Each blockchain type has its own set of traits and applications, enabling organizations and individuals to choose the most suitable blockchain architecture based on their specific requirements for security, scalability, control, and data sharing.

3.4 Blockchain architecture

Blockchain software development is quite difficult and involves the consideration of many factors. There are five layers in the architecture of Blockchain to integrate Blockchain into healthcare applications. There are layers such as (i) Blockchain layer and decentralized ledger technologies (DLT) (ii) API (iii) Interface, and (iv) Application. Figure  11 describes how healthcare participants can safely exchange or gather data using Blockchain by providing the various interface layers of the Blockchain architecture.

The architecture of Blockchain-based healthcare system. API: Application programming interface, SDK: Software Development Kit, DLT: Distributed ledger technology

Blockchain layer It is a platform for architectural design and a communication channel. It is called a base protocol layer and is accomplished via the use of technologies like Ethereum, Hyperledger, R3 Corda, Ripple, and Quorum (Androulaki et al. 2018 ; Benji and Sindhu 2019 ). The consensus protocol is an approach used for implementing this Blockchain layer (King and Nadal 2012 ; Hasselgren et al. 2021 ).

DLT layer A decentralized ledger technology Layer (DLT) is a network of users, or nodes, that share and manage a database of records. It is tampered-resistant, and encryption methods are used for the safe and secure storage of any data. It will store data that is unchangeable and tamper-proof. IPFS, Filecoin, Sia, Stroj, and Swarm are some instances of DLT layers (Orhan et al. 2021 ; Warnat-Herresthal et al. 2021 ).

Application programming interfaces and software development kit layer (API/SDK) It consists of libraries, whereas an SDK is a development kit that enables API usage. Notably, Blockchain API is a critical component of this architecture (Dinh and Thai 2018 ). REST API, WEB3 JS, RPC, Firebase, SOAP, OpenML, Unity, SigularityNET, and CoinMarketCap are examples of APIs that provide efficient mechanisms for connecting to software components or resources (Gropper 2016 ; Zhuang et al. 2019 ). SDK, on the other hand, provides vital building tools such as code libraries, compilers, and documentation to aid developers in software development. Cosmos SDK and Tatum SDK are two examples. Recognizing the importance of both API and SDK, they play a crucial role in allowing effective application integration (Kwon and Buchman 2019 ).

Interface and application layers The interface layer of a healthcare system allows user interaction and access to all features through desktop and mobile applications via this layer. Applications in the interface layer may also provide login and registration functions to authorize users (Zhang et al. 2021b ). The top layer (application layer) enables (healthcare) applications to access and interact with data securely. It covers various healthcare applications like EHR, patient identification management, healthcare supply chain management, clinical trials, biomedical research, and COVID-19 response (Khatri et al. 2021 ).

3.5 Blockchain challenges and its solutions in healthcare

Some of the biggest problems in healthcare that blockchain may tackle include interoperability of electronic health records, patient care, and data privacy concerns (Khatri et al. 2021 ). Blockchain allows efficient data exchange while maintaining data integrity and patient privacy (Katuwal et al. 2018 ). Blockchain networks are now being used in many different industries, one of which is healthcare (Mettler 2016 ; Alhadhrami et al. 2017 ). The three desired features that Blockchain systems try to attain are consistency, decentralization, and scalability (CDS) (Islam et al. 2021 ). In Table  2 , we have presented an overview of key problems in Blockchain-based healthcare applications and their potential solutions.

4 Overview of artificial intelligence: a brief exploration

4.1 artificial intelligence background and evolution.

AI is a domain within computer science focused on the creation of intelligent machines. Originating in the 1950s, AI has transformed by leveraging advances in algorithms, processing powers, and data accessibility, driving the discipline to unprecedented heights and matching human intellect.

The very first aspect of health care is accumulating and evaluating data such as medical records and historical data (Duan et al. 2019 ). Data acquisition is the most extensively needed in AI for training models and ensuring digital automation (Yu et al. 2018 ). AI can successfully handle categorization by mapping nonlinearity between input fluctuations and illness severity (Davenport and Kalakota 2019 , Skandha et al. 2020a , b , Tandel et al. 2020 ). AI methods are used for predicting a patient’s treatment outcomes based on their specific characteristics and the treatment context (Suo et al. 2018 ). It also tries to address the problem of sparse and small datasets in the medical field (Pereira et al. 2021 ). Data is essential to AI and ML algorithms, which perform better with reliable, secure, trustworthy, and credible data (Chen et al. 2018 ). AI in healthcare confronts several obstacles, including ethical use, algorithm biases, data privacy, and trust building. These challenges must be addressed to ensure patient welfare and safety while ensuring data privacy and security.

4.2 Popular AI learning techniques in healthcare

ML is the foundation of AI algorithms, which interpret data, learn from it, and make predictions. AI algorithms are classified into four types (i) supervised learning, (ii) unsupervised learning, (iii) reinforced Learning (iv) federated learning. Machine learning (ML) and deep learning (DL) are two types of AI-supervised learning techniques (Davenport and Kalakota 2019 ). Figure  12 provides a comprehensive taxonomy of learning techniques, illustrating the diverse methods contributing to AI subjects.

Mapping the AI Landscape: A Comprehensive Taxonomy of Learning Techniques. DL-Reg: Deep Learning Regression, NN-MO: Neural Network Multiple Object Regression, KNN: K- Nearest Neighbor, SVM: Support Vector Machine, DT: Decision Tree, RF: (Random Forest), XG: Extreme Gradient Boost, Convolution NN: Convolution Neural Network, RNN: Recurrent Neural Network, GMM: Gaussian Mixture Model, EM: Expectation and Maximization, DEC: Deep Embedded Clustering, JULE: Joint unsupervised learning Clustering, ACOL: Adversarial Learning Clustering, DCC: Deep Continuous Clustering, VaDE: Variational deep embedding Clustering, CCNN: Clustering CNN, RCNN: (Region-based Convolutional Neural Network), GAN: (Generative Adversarial Learning), MRCNN: Mask RCNN, SARSA: (State Action Reward State Action)

4.2.1 Supervised learning

In this learning, each data point is assigned a label by subject-matter experts who label the data as a whole using two techniques regression and classification. Several methods have been devised to address diverse regression challenges, each with unique features and applications.

4.2.2 Regression

It is a supervised learning problem to predict a continuous numerical output. Regression techniques use the labeled cohort to predict the next data point using parametric-based techniques such as linear regression or logistic regression (Jamthikar et al. 2020 ; Teji et al. 2022 ).

Linear regression Determines a linear connection between input variables to predict continuous numerical results. Linear regression is used in healthcare to predict patient outcomes, such as calculating illness progression based on multiple parameters (Kan et al. 2019 ). In addition to that linear regression in healthcare is used for analyzing the patient’s history, especially in clinical trials.

Logistic regression Despite its name, logistic regression is employed for binary classification, evaluating the likelihood of an instance belonging to a given class (Panda 2022 ). It is used in healthcare to forecast whether a patient will have a given ailment or not.

Ridge regression A regression approach that uses regularization to avoid overfitting, which is especially beneficial when dealing with multicollinearity. Ridge regression is a tool used in healthcare to predict intricate interactions between several health indicators (Saqib 2021 ).

Lasso It encourages smaller models by picking important characteristics and avoids overfitting. It is used for medical feature selection, disease diagnosis, and prediction (van Egmond et al. 2021 ).

Artificial neural networks (ANN) Neural networks, which are influenced by the functioning of the human brain, learn intricate patterns through connected layers of nodes. Image recognition, predictive modeling, and pattern analysis are examples of applications in healthcare (Kumar and Kumar 2013 ) .

Deep learning regularization (DL-Reg) It is a technique for reducing overfitting in deep neural networks by restricting model complexity. Important for generalization in medical applications such as image processing and patient prediction (Huang et al. 2017 ).

Multiple regression ( multireg) It is a statistical approach that models the connection between a dependent variable and numerous independent variables (Allison 1999 ). Analyzing the impact of many factors on health outcomes, such as investigating the impact of patient characteristics on therapy response (Agarwal et al. 2022 ).

Neural network multiple object regression (NN-MO) A specific neural network approach for predicting several object-related variables in regression problems at the same time. In healthcare, NN-MO (Cui et al. 2018 ) can be used to predict various health-related outcomes at the same time, such as patient vitals, disease progression, or treatment reactions that occur.

4.2.3 Classification

Classification is another type of supervised learning problem in which the goal is to predict the category class or label of an input. In healthcare, classification aids in diagnosing diseases, recognizing anomalies, predicting risks, improving decision-making, and prompt interventions, ultimately enhancing patient outcomes (Peng et al. 2011 ; Castelli et al. 2018 ).

Support vector machine (SVM) SVM classifies data by determining the hyperplane in the space of high dimensions that best separates multiple classes. In healthcare, it is used to classify medical information or images, which aids in illness diagnosis and treatment planning (Srivastava et al. 2019 ; Jamthikar et al. 2021a , b ; Konstantonis et al. 2022 ).

SVM is the most popular and widely used algorithm in health care for classification problems.

Naive Bayes It is a probabilistic technique based on Bayes' theorem that assumes independence among features and is often used for classification tasks (Webb et al. 2010 ). Employed in healthcare for activities such as email filtering in clinical communication and illness prediction based on numerous patient characteristics.

Convolutional neural network (CNN) It is a deep learning technique developed for image processing and pattern identification that uses convolutional layers to automatically learn hierarchical features (Chua and Roska 1993 ). Used in medical imaging to detect cancers or abnormalities in radiological images, improving diagnostic accuracy. The general alexnet-based CNN architecture is given in Fig.  13 .

The general alexnet based CNN architecture (Courtesy of Dr. Luca Saba et al. 2021a , b , c , Italy)

Decision tree It is a model in which judgments are made based on characteristics, leading to a conclusion or prediction. Medical decision-making applications include forecasting patient outcomes and choosing treatment options based on patient characteristics (Tandel et al. 2020 ; Skandha et al. 2022a , b ). A decision tree in healthcare is used for finding the comorbidities and strong characteristics of the disease.

Random forest It is a collaborative instructional technique that builds a large number of decision trees and blends their outputs to increase overall forecast accuracy. In healthcare, for tasks such as illness risk prediction, the combined power of numerous decision trees is used to produce more robust results (Maniruzzaman et al. 2019a , b ; Jamthikar et al. 2020 ).

Hybrid Hybrid models are the combination of two techniques of ML or DL, frequently using strengths from different algorithms to improve overall performance. In healthcare, hybrid models may incorporate diverse algorithms for tasks such as illness prediction, predictive accuracy optimization, and model resilience (Jena et al. 2021 ; Suri et al. 2021a , b , 2022a ; Das et al. 2022 ; Skandha et al. 2022a , b ). Figure  14 depicts an example hybrid architecture of Inception-ResNetV3.

The hybrid architecture of Inception-ResNetV3 (Courtesy of Dr. Siva Skandha et.al (Skandha et al. 2022a , b ), India)

XGBoost It is an improved gradient boosting technique that excels at managing complicated connections among data, frequently surpassing other boosting approaches. In healthcare, it is used for a variety of activities including forecasting patient outcomes and improving decision support systems (Jamthikar et al. 2021a , b ; Jamthikar et al. 2021a , b ).

AdaBoost It is an ensemble learning approach that combines weak learners to build a strong learner by adding weights to misclassified cases. Used to improve accuracy in tasks such as forecasting patient outcomes or detecting disease risk factors (APC 1871 ; Acharya et al. 2013a , b , c , d ).

k-Nearest Neighbor (kNN) It is an easy method that employs the vast majority of class among its k-nearest neighbors in feature space to classify data items. In healthcare, it is used for tasks like patient similarity analysis and illness categorization based on comparable instances (Shouman et al. 2012 ). This classifier is also another popular classifier in healthcare, it is used as a statistical approach for clinical trials for classifying and characterizing the diseases.

Supervised capsule neural networks (CapsNets) pioneered by Sabour et al. ( 2017 ), have revolutionized computer vision tasks through supervised learning by introducing capsules within CNNs to capture specific features and enhancing feature representations (Paoletti et al. 2021 ). By focusing on intricate spatial relationships and providing interpretable features, CapsNets overcome the limitations of traditional CNNs (Mukhometzianov and Carrillo 2018 ). CapsNets are useful in diagnosing COVID-19 from medical images like X-rays and CT scans (Toraman et al. 2020 ; Ragab et al. 2022 ) identifying patterns and abnormalities, and predicting disease severity (Afshar et al. 2020 ; Tiwari and Jain 2021 ; Gupta et al. 2022 ). Their ability to interpret complex visual information aids in understanding and analyzing COVID-19 medical images (Farki et al. 2021 ; Monday et al. 2022 ). Moreover, CapsNets have potential in drug discovery, enabling the prediction of drug efficacy and identification of potential candidates against COVID-19 (Jin et al. 2023 ). Figure  15 illustrates the Sample Architecture of a Global Machine Learning Healthcare System.

Samle Architecture of a Global Machine Learning Healthcare System. (Courtesy of Dr. Luca Saba et al. 2021a , b , c ), Italy)

4.2.4 Supervised graph learning

Graph learning is a multidisciplinary technique that models and analyses complex interactions in data sets using graph-based structures. It uses clustering algorithms, graph embedding techniques, and Graph Neural Networks (GNNs) (Gori et al. 2005 ). GNNs have found practical applications in supervised tasks, especially node classification in social networks. Designed to process data organized in graph structures, GNNs excel in tasks like link pre-diction node classification, and community detection (Gu et al. 2021 ; Tandon et al. 2021 ). In the context of COVID-19, GNNs have been effectively applied to predict outcomes (MacLean 2021 ; Kumar et al. 2022a , b , c , d ). By leveraging graph structures, GNNs can capture relevant relationships and dependencies, leading to improved predictive performance (Palanivinayagam et al. 2022 ). Graph learning is useful in COVID-19 scenarios for disease spread dynamics, healthcare resource allocation, and public health interventions. It also has applications in drug development, molecular biology, and social network research.

4.2.5 Unsupervised learning

Unsupervised learning approaches aid in the identification of patient groups with similar features, allowing for more individualized treatment strategies (Molinari et al. 2012 ). Unsupervised methods in AI utilize two techniques: clustering and segmentation, which combine related elements in a dataset and remove areas of interest before labeling the information (Araki et al. 2016 ).

4.2.5.1 Clustering

Clustering is a method that groups comparable data points based on certain qualities or attributes without previous labeling (Xie et al. 2016 ). It is used in healthcare to group patients who have similar medical characteristics, allowing for individualized treatment plans and personalized treatments (Strauss et al. 1973 ).

Deep embedded clustering (DEC) It is an unsupervised deep learning approach that blends deep neural networks with clustering to learn high-level representations of data while also performing clustering. DEC can be used for patient stratification, classifying people according to hidden patterns in their health data, perhaps leading to more personalized therapy (Wu et al. 2021 ).

4.2.5.2 Joint unsupervised learning (JULE)

JULE is an unsupervised method that simultaneously learns a feature representation and a clustering assignment (Moriya et al. 2018 ). It is used in the healthcare sector to identify patient groups with similar characteristics and to uncover hidden structures in complex medical data.

Agglomerative clustering with online learning (ACOL) It is an agglomerative clustering method that dynamically adjusts to incoming data by using online learning (Wu et al. 2020 ). To ensure that the clustering model adapts to new data, ACOL is useful in healthcare contexts where data is continuously updated, such as patient records.

Deep continuous clustering (DCC) It is an unsupervised learning approach that combines clustering and deep learning for continuous data (Shah and Koltun 2018 ). DCC may be used to analyze data from continuous patient monitoring to find trends and patterns in physiological parameters that can be used to spot abnormalities early.

Variational deep embedding (VaDE) It is a generative model that combines Gaussian mixture models with variational autoencoders (Ji et al. 2021 ). It can assist with tasks like anomaly detection and disease cluster identification by helping to capture complicated relationships in patient data.

Capsule-based clustering neural network (CCNN) It is a clustering technique designed for unsupervised learning tasks. Using comparable characteristics or structures, CCNN can help in automatically classifying images for study in the medical field (Zhang and Wu 2018 ).

K-means clustering (KMean) It is a standard clustering approach that divides data into k groups according to the mean values of the feature sets. It may be applied to healthcare tasks such as grouping patients according to clinical characteristics into discrete groups (Silitonga 2017 ) .

Gaussian mixture model (GMM) It is a probabilistic model that makes use of a mixture of Gaussian distributions to represent a variety of complicated data distributions in an adaptable manner (Bouman et al. 1997 ). GMM helps represent a variety of patient demographics and encapsulates data distribution uncertainty.

Expectation–maximization (EM) It is a framework for estimating maximum likelihood in probabilistic models, particularly useful in healthcare for latent health status modeling and data imputing from patient records, addressing missing data (Moon 1996 ).

Fuzzy This clustering offers a more nuanced representation by allowing data points to belong to multiple groups with varying membership degrees, particularly useful in cases of overlapping clinical symptoms (Höppner et al. 1999 ).

Hierarchical This clustering is a method used in healthcare to organize patient data in a tree-like hierarchy, revealing both broad and fine-grained patterns, and can be applied to reveal relationships between data points (Papin et al. 2021 ).

4.2.5.3 Segmentation

Segmentation is the process of breaking down a picture or set of data into distinct areas or segments according to attributes. This enables a more thorough examination of each component. To recognize and distinguish features like organs, tumors, or blood arteries, segmentation is applied to medical images (Aggarwal et al. 2011 ; Moriya et al. 2018 ).

Region-based convolutional neural network (RCNN) Region suggestions are used by the RCNN object detection framework to locate and categorize items in an image (Balasubramanian 2022 ). Using RCNN, anomalies or specific features in medical images may be found and localized.

Generative adversarial network (GAN) A generative model known as a GAN is made up of a discriminator and a generator that is taught concurrently via adversarial training (Aggarwal et al. 2021 ). GANs may be used to create artificial medical images for training or to enhance data in medical imaging.

Encoder and decoder Encoder–decoder architectures are used in medical image segmentation models to capture hierarchical features from input data and reconstruct the segmented output, enabling the extraction of intricate features (Gao et al. 2019 ).

UNet CNN architecture created for semantic segmentation is known as UNet. Basic U-Net architecture with feature map sample sizes is given in Fig.  16 . It is made up of an expanding path, a bottleneck, and a shrinking path. UNet (Jain et al. 2022 ; Sharma et al. 2022 ), and hybrid UNet (Jain et al. 2021 ; Suri et al. 2022a , b ) methods are widely used in medical imaging.

Basic U-Net architecture with feature map sample size (Courtesy of Dr. Luca Saba et al. 2021a , b , c , Italy)

4.2.5.4 Mask R-CNN (MRCNN)

It is a modification of RCNN that enables instance segmentation by incorporating a mask prediction branch. It is useful in medical imaging for cell detection, and accurate separation of distinct instances, such as identifying multiple cancers in a single picture (Fujita and Han 2020 ).

Semantic This divides an image’s pixels into distinct groups, aiding in organ segmentation and target tissue identification in medical imaging (Scheikl et al. 2020 ).

Instance It is a method that classifies pixels and distinguishes between instances of the same class, useful in healthcare settings like identifying multiple nodules in lung CT scans (Frade et al. 2022 ).

Panoptic Panoptic segmentation unifies semantic and instance segmentation, providing a comprehensive understanding of both stuff (non-specific regions) and things (specific instances) (Kirillov et al. 2019 ). It allows a holistic analysis of medical images, incorporating both general structures and specific entities within the same framework.

4.2.5.5 Unsupervised capsule networks (CapsNets)

In the domain of unsupervised learning, CapsNets exhibit exceptional versatility and effectiveness. It facilitates a diverse range of applications such as clustering, unsupervised feature learning, generative modeling, and anomaly detection (Piciarelli et al. 2019 ; Fan et al. 2020a , b ). CapsNet’s proficiency in capturing intricate data structures and providing interpretable features makes it a compelling approach for extracting valuable insights from unlabeled medical images (Sharma et al. 2023 ). This potent and interpretable tool holds promise for addressing COVID-19 challenges without extensive labeled data (Sandu and Karim 2020 ).

4.2.5.6 Unsupervised graph learning

Unsupervised graph learning, a facet of machine learning, uncovers patterns in graph-structured data without labeled information (Xia et al. 2021 ). Graph learning approaches, particularly GNNs, have been successfully applied in various domains, including recommendation systems (Fan et al. 2019 ; Wang et al. 2021a , b ), social network analysis (Hamid et al. 2020 ), drug discovery (Bongini et al. 2021 ), and computer vision (Chen et al. 2022 ). In COVID-19 detection, GNNs have proven effective in unsupervised graph tasks (Dan-Sebastian et al. 2020 ; Chandra et al. 2023 ) and in predicting outcomes (Zhou et al. 2020 ). Recent research is exploring the integration of blockchain technology to enhance security and privacy when sharing weight files, input images, and predictions, aiming to address data security concerns in healthcare (Liu et al. 2020a , b ; Wang et al. 2020 ).

4.2.6 Reinforcement learning (RL)

RL including Q-learning, is used in medical imaging, particularly during the COVID-19 pandemic, to identify patterns in unlabeled data. Figure  17 depicts a model of the reinforcement learning architecture. This error-by-trial learning approach, which includes Q-learning, is often employed with unlabeled data. In Shang and Li ( 2022 ), the authors demonstrated a hybrid combinatorial remanufacturing technique that produced RL models relying on Q-learning and a deep Q-network. In (Cockrell et al. 2022 ), the authors use deep reinforcement learning (DRL) similar to game-playing AI. This DRL uses deterministic policy gradient using a deep model while manipulating the six cytokines such as tumor necrosis factor, four famous interleukins (1, 4, 8, and 12), and interferon-gamma. In (Böck et al. 2022 ) the authors used RL for the treatment of sepsis, a medical life-threatening emergency during the COVID-19 pandemic.

Sample Reinforcement learning architecture. (Courtesy of Dr. Luca Saba et al. 2021a , b , c , Italy)

Few authors also used the DRL framework to prioritize vaccines in COVID-19 applications (Bushaj et al. 2022 ), comparing age-based, comorbidity-based, or random-based paradigms.

4.2.7 Federated learning (FL)

ML with a decentralized structure, based on a star topology is known as FL (Yang et al. 2019 ). In FL, the models will be trained on individual user-edge devices, with the resulting updates being delivered to a central server (Lu et al. 2020 ; Shen et al. 2020 ; Feng et al. 2021 ). Predictions in subsequent cases are made using the revised model. In healthcare, the FL model shines in predicting illnesses using data sources with complicated pattern training, such as COVID-19 (Samuel et al. 2022 ) because recent advances in FL lead to fog computing (FC), intelligent devices can now collaborate with the server in the cloud (Celesti et al. 2020 ).

4.3 Artificial integration in healthcare: exclusion of blockchain

AI integration in healthcare without Blockchain can improve diagnostics, personalized medicine, and operational efficiency. However, challenges like data security, privacy, and interoperability persist. Ensuring patient information integrity is crucial, and the absence of Blockchain may hinder the establishment of a secure, decentralized data infrastructure, potentially limiting the seamless sharing and trustworthiness of healthcare data across different entities and systems. Table 3 discusses the areas of AI applications specific to healthcare, its challenges, and the real-world implementation considerations.

4.4 Artificial intelligence’s potential hazards in the healthcare system

Common errors that cause artificial intelligence to fail are as follows:

4.4.1 Using the incorrect data

Despite the enormous progress with the adoption of AI specific to healthcare, there are several significant challenges, especially data-related ones (Banerjee and Chanda 2020 ). Data must be precise and error-free before it can be used in AI applications. Moreover, inadequate and incorrect data for training and testing AI models may lead to bias in algorithms, especially unequal distribution of classes (FitzGerald and Hurst 2017 , Norori et al. 2021a , b ). For instance, an AI model trained on white-skinned patients may not be as accurate when applied to patients of different races. An incorrect diagnosis made by an AI-powered diagnostic system might have fatal repercussions.

AI is not always unbiased, and even slight prejudice can impact outcomes. Researchers use data augmentation to expand image access, but this doesn't always ensure strong validation (Monshi et al. 2021 ). To prevent bias, AI should be trained using diverse data, which can be achieved using Blockchain (Chen et al. 2018 ; Zheng et al. 2019 ). This combination could reduce bias and improve future prediction models.

4.4.2 Security

Data in healthcare include clinical reports, scientific studies, patient health records, and diagnostic details (Churi et al. 2021 ). They have been created daily in large volumes (especially due to mobile devices) during the past decade and must be stored securely for the future. Since information is precious, it must not only be stored securely but also transferred safely. Data breaches can be a common problem while transferring data (Dilmaghani et al. 2019 ). In these situations, Blockchain’s strong and dependable storage can safely convey data for AI training models and prediction (Zhang et al. 2021a , b ).

4.4.3 Data aggregation and automation

AI algorithms can never be relied on completely unless they are first constructed and then trained on a substantial amount of relevant and diverse data (Lee et al. 2017 ). However, accessing high-quality clinical datasets is challenging due to strict regulations and protections. Hospitals have limited access to EHRs due to the Food and Drug Administration (FDA) and the Health Insurance Portability and Accountability Act (HIPAA) (Shuaib et al. 2021 ). EHR exchange across databases requires additional work to ensure compliance. This has led to ongoing discussions on who can access protected health information. Acquiring and using a database with thousands of specific photos can be challenging, especially when working with uncommon ailments. Blockchain can help AI in data aggregation and update the network, accordingly, making AI more reliable in treating rare diseases (Dillenberger et al. 2019 ).

4.4.4 Privacy

Privacy and patient data problems are important considerations involving patient data gathering (Tith et al. 2020 ). Researchers have safeguards in place to protect patient data, but unscrupulous hackers still try to gain access to the data (Yampolskiy and Spellchecker 2016 ). AI’s ability to predict patient information even when the algorithm was not provided with such data also threatens patients’ privacy. Recently, the AIIMS reported that the entire network got compromised, and it was suspected that the ransomware assault affected the data of three to four hundred million patients (Mohurle and Patil 2017 ). Using Blockchain authorized people can have a copy of the information on their node and can get access to the data as and when needed and this can avoid this type of situation (Feng et al. 2019 ).

5 Synergies unveiled: the interdependence of artificial intelligence and blockchain technologies

This exploration deconstructs their symbiotic connection, highlighting magnified strengths for game-changing advances in security, transparency, and decentralized intelligence uncovering the collaborative future of technology.

5.1 Blockchain for artificial intelligence, decentralized computing in healthcare

The fusion of Blockchain with AI results in secure data sharing and marketplaces for AI systems. Combining Blockchain and AI has obvious benefits and provides decentralized computing for AI (Dinh and Thai 2018 ). Blockchain protects data, enables us to audit all intermediary steps, and even allows users to monetize their data (Chen et al. 2019 ; Jennath et al. 2020 ). Blockchain-enabled data marketplaces promote secure data incorporation from healthcare participants and vendors, enhancing AI training. Blockchain is essential for AI and other applications such as the financial market (Zhang et al. 2020 ), the Internet of Everything (IoE) (Singh et al. 2020 ; Alrubei et al. 2021 ), edge computing (Fan et al. 2020a , b ; Shen et al. 2020 ; Manogaran et al. 2021 ), and fog computing (Qu et al. 2020 ). To protect the IoE, an access management system architecture has been proposed (Bera et al. 2020 ). So, it is evident that Blockchain can assist AI in becoming more independent, trustworthy, and intelligent. (Dinh and Thai 2018 ; Zhang et al. 2021a ). Blockchain and AI are combining to create secure data sharing and marketplaces for AI systems. This decentralized computing approach protects data, audits intermediary steps, and allows users to monetize their data. Blockchain-enabled data marketplaces promote secure data incorporation from healthcare participants and vendors, enhancing AI training. Blockchain is essential for AI applications in financial markets, IoE, edge computing, and fog computing. To protect IoE, an AI-based Blockchain-envisioned access control architecture has been proposed. This fusion of Blockchain and AI can help AI become more independent, trustworthy, and intelligent, making it a valuable tool for various applications.

5.2 Artificial intelligence for blockchain in healthcare

By combining AI with Blockchain, the flaws of these technologies may be effectively corrected. Blockchain creates a safe and transparent distributed personal data marketplace by enabling secure data exchange between organizations (Mamoshina et al. 2018 ). On the other hand, AI technology enables the privacy-preserving personalization of patient records (Suo et al. 2018 ). The combination has been used to protect healthcare data privacy (Singh et al. 2022a , b ), securely transfer imaging data (Orhan et al. 2021 ), a donor organ transplant system (Morande and Marzullo 2019 ), and even in healthcare workflow in a telemedical laboratory (Celesti et al. 2020 ), cardiovascular medicine (Krittanawong et al. 2020 ), novel coronavirus disease 2019 self-testing (Mashamba-Thompson and Crayton 2020 ). Secure and scalable Blockchains can be developed by interdependency on AI. Web 3.0 uses machine learning, AI, and Blockchain to improve human communication (Leeming et al. 2019 ). Figure  18 gives an overview of the reliance of AI on Blockchain and Blockchain on AI in various application objectives. This dependence may be beneficial to the healthcare system by reducing the amount of rework and reconciling making it an immensely powerful tripod for the future.

Dependency of AI and Blockchain: Blockchain for AI, and AI for Blockchain

The combination of AI with Blockchain is essential because it builds a strong basis for the future of technology. This cooperation not only improves decision-making security and transparency but also instills confidence through verifiable and immutable records. Dynamic decision-making combined with data integrity develops an accountability culture, while distributed intelligence assures operational efficiency across decentralized networks. The anti-tampering and privacy-protection features address crucial issues, offering a safe foundation for sensitive data.

This integration streamlines processes, maintains dependability, and reshapes the landscape of industries, offering a future where trust, security, and efficiency meet effortlessly. Table 4 provides insights into AI and Blockchain properties and the astonishing benefits that result from their synergistic combination. The study explores the intricate interplay of various technologies, concentrating on their potential to improve integrity, openness, and efficiency in decision-making processes, thereby laying the foundation for a competent future.

6 Blockchain integration with AI-enabled healthcare: the tripod

The fusion of AI and blockchain technology specific to healthcare is transforming the healthcare process, enhancing clinical operations, and service intensity, and managing predictive activities. This fusion reduces healthcare-related risks, making it a future healthcare tripod. This summary covers 51 articles on the integration of AI and blockchain in healthcare applications, highlighting their impact on diagnoses, treatment planning, data security, and administration.

6.1 Blockchain feature specifications in AI-enabled healthcare studies

Blockchain is indeed the technology that powers cryptocurrencies (King and Nadal 2012 ; Narayanan et al. 2016 ) and is being used in AI-enabled healthcare studies to securely transfer healthcare-related data and maintain patient privacy (Mantey et al. 2021 ). Table 5 summarizes the usage of blockchain features considered in AI-enabled healthcare studies. Smart contracts, which combine immutability, transparency, and decentralization, are a crucial feature in data authorization, particularly beneficial in healthcare for enhanced efficiency and transparency, making them a significant improvement in any industry (Zou et al. 2019 ).

Various AI-enabled blockchain attributes are essential for accelerating data-specific AI application development. Figure  19 explores the fusion of AI-enabled Blockchain features in healthcare, highlighting its privacy and security benefits. Key attributes include immutability, availability, integrity, and authentication, while AI attributes like accuracy and prediction are crucial.

Blockchain features for AI-enabled healthcare system

6.2 AI-enabled blockchain applications and case studies in healthcare: enhancing healthcare operations and patient outcomes

In the ever-changing environment of healthcare, the fusion of these technologies has sparked novel solutions. This section delves into AI-enabled blockchain applications and case studies, demonstrating their critical role in improving healthcare operations and maximizing patient outcomes.

6.2.1 Pandemic: detection and diagnosis to monitoring and prediction

COVID-19 initially posed significant challenges to the healthcare system due to infrastructure deficiencies, including a lack of integrity, immutability, and audit framework (Hamze 2021 ; Musamih et al. 2021 ). AI algorithms combined with blockchain technology enable real-time analysis of healthcare data, facilitating early detection, diagnosis, and monitoring of infectious diseases. To address these issues, the integration of Blockchain and AI has been crucial, enhancing the integrity and immutability of healthcare data, and ensuring a secure and transparent framework (Meghla et al. 2021 ; Kamenivskyy et al. 2022 ) . This integration streamlines clinical operations by enabling timely interventions and containment measures, ultimately improving patient outcomes and public health, particularly in response to the COVID-19 pandemic. In the Rahman et al. ( 2020 ) study, the application focused on classifying Internet of Health Things (IoHT) data related to COVID-19 using Deep Neural Networks (DNN) within a Consortium blockchain. This integration aimed to ensure authentication, privacy protection, and data integrity, thereby enhancing clinical operations and patient outcomes. Similarly, Warnat-Herresthal et al. ( 2021 ) applied DNN-Swarm Learning and RNA sequencing in a private blockchain setting to predict and analyze COVID-19, addressing concerns of confidentiality.

Moreover, studies such as Muhammad and Hossain ( 2021 ) emphasized data privacy and security through Edge computing and Convolutional Neural Network (CNN) models. These efforts streamline clinical operations by ensuring the confidentiality and integrity of patient information, eventually fostering better patient health outcomes. Additionally, Tanwar et al. ( 2021 ) explored the use of long short-term memory (LSTM) techniques and other AI methodologies to enforce social distancing measures, enhancing security within healthcare environments.

Further, collaborations like Bera et al. ( 2021 ) utilized support vector machines (SVM) and RCNN models within Public blockchain frameworks to monitor COVID-19 patients in home isolation, ensuring security and trust. Meanwhile, efforts by Kumar et al. ( 2021a , b ) and Mohsin et al. ( 2021 ) in Private blockchain environments leveraged machine learning and FL methods to identify COVID-19 patients using CT scans and to share and update COVID-19 data safely and reliably, respectively, thereby enhancing security and privacy. Similarly, other studies have revealed that by ensuring data security, privacy, and integrity, these technologies can enhance diagnostic accuracy, treatment efficacy, and patient care delivery. This approach can help healthcare organizations respond to COVID-19 challenges and establish a more patient-centric future.

A total of 16 research articles from 2020 to 2023 assess the fusion of AI and blockchain in healthcare, focusing on COVID-19-related situations, across various applications. Table 6 shows a comprehensive examination of AI and blockchain fusion in healthcare for combating COVID-19.

6.2.2 Blockchain integration with AI: data redundancy and augmentation specific to COVID-19

Data Redundancy is a common problem in healthcare and we are aware that manually collected patient records can have redundancy and duplication (Gupta et al. 2019 ). The medical imaging field faces significant challenges due to the limited number and variety of samples in small datasets (Saba et al. 2019 ). Deep learning networks like CNN require large data for training, leading to data augmentation during the COVID-19 pandemic for improved detection using random and duplicated datasets (Monshi et al. 2021 ). Data duplication, a technique used in AI frameworks (Sanagala et al. 2021 ) aids in generalizing AI solutions and preventing overfitting problems (Agarwal et al. 2021a , b ).

Blockchain can help prevent overfitting across multiple classes by enhancing data accuracy through data augmentation techniques like flipping or rotating images (Tian et al. 2021 ). Based on the findings of the European Parliamentary Research Service, Blockchain is regarded as one of the most important current technologies for COVID-19 (Mihalis 2020 ). During an epidemic, inadequate counting and data gathering were prevalent, exacerbated by late private clinic reports and non-automated processes (Kalla et al. 2020 ). Adopting Blockchain promotes trust across people, organizations, governments, and continents. Providers and users send anonymized patient data via the Blockchain paradigm (Zhuang et al. 2020 ). Countries can use private Blockchain solutions or government health databases to send daily test results and other information, but both must maintain hash values (Nakamoto 2008 ). Blockchain technology can be effectively used to monitor and combat infectious diseases like COVID-19, and also for other applications like patient data exchange, and contact tracing (Kalla et al. 2020 ).

As the Blockchain network allows only tamper-proof data sharing (Cheng et al. 2020 ), this may result in data exchange safeguarded by Blockchain and further advance AI (Bhattacharya et al. 2019 ; Kumar et al. 2021a , b ). Therefore, enhanced AI may result in increased data security and efficiency (Wang et al. 2019a , b ). The study aims to improve healthcare models by combining Blockchain with AI, resulting in (i) superior training (ii) increased accessibility of data required for AI enhancement, and (iii) the distribution of exclusive AI generalization algorithms among healthcare providers, resulting in a decentralization of datasets (Kumar et al. 2022a , b , c , d ) thereby enhancing its functionality and effectiveness by this superior tripod design.

AI and blockchain technologies are revolutionizing healthcare by improving security, interoperability, and intelligence in managing health data. AI safeguards medical information confidentiality, while blockchain analyzes large datasets for critical insights. Research shows these technologies significantly enhance privacy and security in EHR and PHR records.

6.2.3 Case studies demonstrate the elimination of falsification and forgery through AI and blockchain

Blockchain integrated with AI algorithms eliminates the falsification and forgery of medical records by ensuring the authenticity and confidentiality of healthcare data. The focus of research conducted by Kim and Huh ( 2020 ) is on improving anonymity and enhancing the sensitivity, availability, and security of AI in medical records. Their objective was to ensure the authenticity of medical records, including EMRs, PACS data, and PHRs by preventing forgery and fabrication. They achieved this by utilizing a mini-batch dataset, and randomly selecting some training data to aid in the learning process. This integration streamlines clinical operations by improving data accuracy, enhancing patient safety, and fostering trust in healthcare systems, ultimately leading to better patient outcomes.

6.2.4 Case studies demonstrating mitigation of data breaches, manipulation, and privacy through AI and blockchain

Traditional databases, relying on a single point of failure, are susceptible to hacking and data breaches. In contrast, the blockchain’s distributed network disperses data across multiple nodes, thwarting malicious tampering. Empirical case studies demonstrate how using these technologies can preserve privacy, guard against security lapses, and safeguard confidential information across a range of industries, including healthcare.

Kumar et al. ( 2022a , b , c , d ) presented a study focusing on enhancing attack detection and preventing data breaches within industrial healthcare systems by integrating permissioned blockchain and smart contracts. The utilization of cryptographic techniques and consensus mechanisms inherent in blockchain technology establishes a tamper-resistant and transparent ledger of transactions. This enhances the detection of malicious activity and prevents unauthorized alterations to sensitive healthcare data. Additionally, the implementation of smart contracts serves as automated protocols governing data access, sharing, and authentication, thereby reducing the risks associated with unauthorized breaches or fraudulent activities. While the study provides a comprehensive approach to enhancing security and integrity within industrial healthcare systems, scalability and comprehensive comparisons with existing methods remain areas for further exploration and refinement.

Furthermore, Alzubi et al. ( 2022 ) introduced a privacy-enhancing system that amalgamates DL, blockchain technology, and FL to address concerns surrounding EHR privacy and security. By leveraging CNN and FL techniques, the study aimed to achieve high accuracy rates, scalability, data privacy, and detection of malicious activity within healthcare systems. A key contribution of the study lies in its proposed model designed to identify abnormal users and restrict their database accessibility, thus preventing the risk of unauthorized access and data breaches to sensitive EHRs. The model utilizes DL algorithms to analyze user behavior patterns and identify deviations indicative of potential malicious intent or abnormal activity. With blockchain fusion, the system ensures the immutability and transparency of access logs, providing a comprehensive audit trail for monitoring user interactions with EHRs. The integration of FL further enhances privacy protection by enabling decentralized model training across multiple data sources thereby avoiding the need for aggregated data in a central location. This approach ensures that sensitive patient data remains localized and encrypted.

Lakhan et al. ( 2023 ) addressed the challenges faced by dynamic Internet of Medical Things (IoMT) systems in healthcare applications, particularly regarding data fraud in distributed environments. The study introduced an innovative framework that employs dynamic heuristics named FL-BETS (FL-based Blockchain-enabled Task Scheduling) to balance load, manage energy consumption, and meet deadlines across cloud and fog nodes. FL-BETS aims to ensure the integrity and privacy of data while minimizing energy consumption and delay, thus optimizing healthcare workload management. The study evaluates FL-BETS against existing blockchain and FL mechanisms, demonstrating superior performance in energy efficiency, data validation, fraud analysis, and meeting healthcare application constraints. Similarly, Reegu et al. ( 2023 ) demonstrated that blockchain-based EHR systems reduce data breaches compared to traditional centralized databases and also minimize the risk of cyberattacks. As we continue to explore innovative approaches, the synergy between blockchain and AI will undoubtedly shape a more secure and resilient digital landscape for the future.

6.2.5 Patient privacy and data security through AI and blockchain

The fusion of AI with blockchain technology appears to be a viable option for improving data security in healthcare systems (Wang et al. 2019a , b ). Researchers have carefully investigated novel strategies to improve data security, notably in the realms of (EHRs/PHRs) (Jennath et al. 2020 ). AI algorithms serve as key enablers in enhancing data security by proactively identifying and mitigating potential threats. While AI algorithms are adept at evaluating large volumes of data and recognizing patterns suggestive of security problems, they are also vulnerable to manipulation and exploitation by malicious actors (Yampolskiy and Spellchecker 2016 ). Furthermore, AI algorithms may struggle to ensure the integrity and immutability of data, making them insufficient as standalone solutions for data security (Jensen et al. 2020 ). The flaw in relying solely on AI for data security lies in its vulnerability to adversarial attacks and limitations in ensuring data integrity and tamper-proofing (Hu et al. 2021a , b ).

Blockchain technology addresses these limitations and enhances data security in several ways. First and foremost, blockchain provides a decentralized and immutable ledger system that protects data integrity and authenticity (Hepp et al. 2018 ). As we know blockchain’s transactions are cryptographically linked, resulting in a tamper-resistant record of data exchanges (Funk et al. 2018 ). The immutability of blockchain data makes it extremely difficult for malevolent people to change or manipulate records without detection (Bathula et al. 2022a , b ). Furthermore, blockchain’s decentralized architecture removes one point of failure and lowers the danger of unwanted access, enhancing the overall resilience of data storage and management systems (Noveck 2011 ). Because of both technologies' complementary qualities, combining AI with blockchain is critical for improving data security in healthcare and other domains (Lu et al. 2021 ; Ali et al. 2023 ). While AI algorithms excel at evaluating data and detecting security risks, blockchain provides a safe and open platform for storing and distributing sensitive data. By combining AI with blockchain, enterprises can use AI’s analytical powers to uncover possible security breaches and abnormalities in real-time, while blockchain provides data integrity, transparency, and immutability (Vyas et al. 2019 ). This integration allows for safe and privacy-preserving data exchange, auditable transactions, and proactive threat detection, thus enhancing overall data security and trust in the digital ecosystem (Mamoshina et al. 2018 ).

The fusion of AI and blockchain increases data security by merging AI’s analytical skills with blockchain’s safe and transparent data management architecture, making it an essential option for protecting sensitive information in today’s digital era (Chen et al. 2018 ; Abou El Houda et al. 2023 ).

6.2.6 Case studies demonstrating patient privacy and data security specific to EHR /personal health records PHR)

In the realm of healthcare, the research on the fusion of AI and blockchain technologies underscores the substantial enhancement of privacy and security in EHR and PHR records.

Several researchers offered cutting-edge frameworks and models to emphasize the privacy and security of patient data such as EHR/ PHR in this thorough analysis of advances in safe healthcare (Jennath et al. 2020 ). They provided a patient-driven data-sharing paradigm that prioritizes privacy without disclosing personally identifying information and is particularly designed for Secure Personal Health and Care Records (PHCR). Alruwaili ( 2020 ) proposed a hybrid strategy combining AI-based intelligent agents and BC to protect EHR databases. Bhattacharya et al. ( 2019 ) proposed Blockchain-based DL as a service for exchanging EHR information about patients with diabetes, and obesity, and improves the security of EHRs by developing cryptographic authentication and signature techniques. The authors Kumar et al. ( 2020 ) proposed a method for choosing miners using supervised learning and AI technology to maintain fairness in a healthcare-based system for mining data. Al-Safi et al. ( 2022 ) in their research, offered a decentralized method to preserve patient privacy in medical data using blockchain technology and utilizing an AI algorithm for classification and accuracy. Furthermore, Alzubi et al. ( 2022 ) provide a privacy-preserving EHR paradigm that identifies typical users and limits database access. Sai et al. ( 2023 ) investigates secure smart healthcare diagnostics by offering an NFT marketplace for patient-controlled healthcare data, utilizing IPFS in FL, and solving privacy problems in cloud-based solutions.

6.2.7 Case studies demonstrating chronic disease management

AI-powered predictive analytics combined with blockchain technology enabled personalized treatment plans and proactive interventions for chronic disease management. This integration streamlines clinical operations by providing timely interventions.

6.2.7.1 Prediction of lung cancer

In their paper Kumar et al. ( 2021a , b ), the authors present a framework integrating DL and Blockchain for decentralized data learning in the context of Lung cancer prediction. For safe real-time data sharing, they made use of a customized smart contract. Zerka et al. ( 2020 ) called this chained distributed machine learning C-DistriM, which is a revolutionary distributed learning approach that blends sequential distributed learning with a Blockchain-based platform. It created a model with high-quality data that can be verified for integrity.

6.2.7.2 Detection of myopic macular degeneration and high myopia.

In their study, Tan et al. ( 2021 ) developed retinal photograph-based DL algorithms and assessed for the diagnosis of myopic macular degeneration and extreme myopia using a retrospective multicohort investigation. Also, they highlighted how deep learning algorithms and Blockchain are leveraged to increase transparency, auditability, and security.

6.2.7.3 Predicting diabetes

In their work El Rifai et al. ( 2020 ), the authors proposed a diabetic predicting solution that addresses private data leakage in FL algorithms by integrating public Blockchain with FL and DL techniques. They demonstrate their methodology using a predictive decision support tool for a dataset (diabetes), emphasizing the importance of protecting sensitive health information.

6.2.8 Case studies demonstrating dietary guidance and customized medical care

AI-driven dietary guidance and customized medical care programs empower patients to make informed lifestyle choices and adhere to personalized treatment plans. Blockchain technology ensures secure data sharing between patients and healthcare providers, streamlining clinical operations and improving patient outcomes through personalized care plans.

The integration of blockchain and AI technologies, as exemplified by the work of Mantey et al. ( 2021 ), has profound implications for streamlining clinical operations and enhancing patient outcomes, particularly in the realm of dietary guidance and customized medical care. Here, blockchain is used by the system to guarantee the privacy and reliability of health data, while AI algorithms analyze individual health information, tailor diet advice, and deliver targeted medication notifications. This combination empowers individuals with specialized care based on their health profiles.

6.2.9 Case studies demonstrating telesurgery or robotic surgery

The fusion of blockchain, AI, and telesurgery signifies a groundbreaking era in surgical innovations. Gupta et al. ( 2020a , b ) introduced a self-managed, secure, transparent, and trustable system for telesurgery or robotic surgery, revolutionizing the healthcare industry by enhancing accuracy, safety, and technology.

6.2.10 Case studies demonstrating clinical/genomic/biomedical research

Kuo et al. ( 2020 ) in their study have developed a model ensemble with hierarchical consensus, using Blockchain-based distribution and level-wise learning. Their findings show that this architecture improves prediction accuracy while preserving privacy . Mamoshina et al. ( 2018 ) use AI and Blockchain to decentralize biomedical research, encouraging continuous health monitoring and accelerating research.

6.2.11 Case studies demonstrating IoMT (Internet of Medical Things) and AI application

The papers discuss the fusion of AI and Blockchain on the IoMT. They focused on developing a healthcare-based system for neural network training, decentralizing healthcare processes through Blockchain-based FL, and personalized FL, and improving healthcare model efficiency, demonstrating the blockchain’s and AI’s potential for IoMT applications.

Samuel et al. present an IOMT privacy architecture (Samuel et al. 2022 ), highlighting the efficiency of AI-enabled big data analytics in safely examining patient data connected to COVID-19. A two-stage FL system for IoMT devices is presented in Lian et al. ( 2022 , 2023 ), which makes use of blockchain to improve training accuracy and privacy over a dispersed network suggests the FL-BETS framework for healthcare, which protects patient privacy and makes fraud detection possible. A decentralized IoMT learning paradigm for real-time patient monitoring is presented in Khan and AbaOud ( 2023 ), along with privacy-focused measures including blockchain authentication and homomorphic encryption. A method for integrating AI into IoMT and improving data privacy for Secure PHCR is put forward (Połap et al. 2020 ). Kalapaaking et al. ( 2023 ), they focused on enhancing the security of healthcare ML data by using privacy-preserving strategies to secure a worldwide healthcare ailment. Jin et al. ( 2021 ) suggested a cross-cluster FL framework through a cross-chain approach (CFL) to cope with privacy leakage and data sparsity issues while ensuring IoMT system efficiency.

6.2.12 Vaccine distribution

The fusion of distributed ledger technology with AI might potentially give the optimal platform for immunization in supply chain management, demand predictions, and vaccine distribution can be further optimized and streamlined (Rahman et al. 2021 ). Blockchain technology helps distribute vaccines by securely and transparently recording transactions (Abbas et al. 2020 ). It also tracks the movement of vaccines from manufacturers to healthcare providers and recipients (Kamenivskyy et al. 2022 ). Blockchain technology can ensure the accurate handling and preservation of vaccine supply information by connecting information silos controlled by suppliers, manufacturers, distributors, and medical professionals (Das 2021 ). The system utilized AI to monitor quality standards, production parameters, and Blockchain data for distribution optimization, trend identification, and vaccination demand forecasting (Antal et al. 2021 ).

Blockchain and AI can also be used to create a comprehensive framework for vaccine distribution, as illustrated in Fig.  20 . The World Health Organization (WHO), manufacturers, suppliers, distributors, national stores, doctors, hospitals, pharmacies, and individual patients participate in vaccination deployment using Blockchain technology (Kazancoglu et al. 2022 ). Blockchain nodes verify information on vaccinations, connecting blocks that precede and follow it (Sunny et al. 2020 ). Post-WHO approval, manufacturers supply vaccines to distributors. Blockchain nodes ensure transparency, linking verified vaccination data through cryptographic hash functions (WHO 2020 ). These systems automate distribution, ensuring timely delivery.

An overall framework for vaccine distribution using Blockchain and AI

ML is used to evaluate vast quantities of data from the Blockchain to forecast the demand for a certain vaccine in a specific location (Das et al. 2021 ; Meghla et al. 2021 ). Early in the pandemic, COVID-19 vaccine delivery was challenging because the system infrastructure lacked integrity, immutability, and audit framework (Hamze 2021 ; Musamih et al. 2021 ). Blockchain and AI have significantly enhanced the accuracy of COVID-19 result forecasts and vaccine traceability by enabling big data analysis on Blockchain (Meghla et al. 2021 ; Kamenivskyy et al. 2022 ) . The vaccine distribution was made more efficient and transparent by Blockchain, ensuring (i) traceability and a thorough audit of the supply chain, (ii) storage, and (iii) delivery conditions (Antal et al. 2021 ).

This fusion of these technologies across various applications and its outcomes is summarized in Table  7 .

6.3 Enhancing healthcare efficiency with AI-blockchain

The fusion of AI and blockchain technologies presents a revolutionary opportunity to enhance healthcare efficiency and effectiveness on multiple fronts. By leveraging AI algorithms to analyze extensive datasets stored securely on blockchain platforms, healthcare systems can realize significant improvements in assessment metrics and diagnostic accuracy, ultimately leading to streamlined processes and better patient outcomes.

Firstly, the combination of AI and blockchain harnesses the power of big data analytics in ways previously unattainable by healthcare providers. AI algorithms excel at processing and interpreting vast amounts of healthcare data, including electronic health records, medical imaging scans, genomic sequences, and real-time monitoring data from wearable devices (Baucas et al. 2023 ). By accessing this wealth of information stored on blockchain platforms, AI systems can identify patterns, trends, and correlations that might otherwise go unnoticed. This comprehensive analysis empowers clinicians to make more informed decisions, tailor treatments to individual patient needs, and optimize healthcare delivery pathways. For instance, a study by Tripathi et al. ( 2024 ) showed how AI might evaluate big datasets to enhance diagnosis precision and provide customized treatment suggestions. Similarly, blockchain’s decentralized nature allows for secure, tamper-proof storage of medical data, ensuring that AI systems have access to reliable and consistent information for analysis (Wu et al. 2022 ).

Moreover, the immutable and transparent nature of blockchain technology ensures the integrity and security of healthcare data throughout its lifecycle (Hölbl et al. 2018 ). By storing data in tamper-proof blocks distributed across a decentralized network, blockchain platforms provide a robust framework for safeguarding sensitive patient information from unauthorized access, tampering, or data breaches (Sonkamble et al. 2023 ). This heightened level of data security instills trust among patients, healthcare providers, and stakeholders, facilitating greater collaboration and information sharing across the healthcare ecosystem.

Furthermore, AI algorithms can leverage the structured and standardized data stored on blockchain platforms to develop more accurate assessment metrics and diagnostic tools. Jennath et al. ( 2020 ) conducted an in-depth analysis of longitudinal patient records, genetic profiles, treatment outcomes, and population health trends. They found that AI-driven systems can detect risk factors, early warning signs, and predictive biomarkers associated with various diseases and conditions. This predictive analytics capability enables healthcare professionals to proactively intervene, monitor disease progression, and personalize treatment plans to optimize patient outcomes. A study by Durga and Poovammal ( 2022 ) highlighted how AI, when combined with blockchain technology, could enhance predictive analytics for chronic diseases, allowing for early interventions and improved patient management strategies.

Thus, the fusion of AI and blockchain technology implies a paradigm change in healthcare delivery. Healthcare systems may improve patient care throughout the continuum by providing precise evaluation metrics and more accurate diagnoses through the analysis of massive amounts of data stored on blockchain platforms. The future of healthcare will be provided with unparalleled opportunities for innovation, efficiency, and impact as we continue to realize the synergistic potential of AI and blockchain.

6.4 Summary of the intersection of blockchain and various AI techniques in healthcare

6.4.1 machine learning-enabled blockchain technologies in healthcare.

Table 8 summarizes research works aimed at integrating Machine Learning-enabled Blockchain Technologies into healthcare applications. The research spans from 2020 to 2022 and addresses a wide range of topics, including telesurgery, patient data exchange, COVID-19 analytics, and vaccination delivery. AI approaches, illness categorization algorithms, and data integrity by smart contracts on the blockchain are among the main contributions to these studies. However, there are constraints, insufficient empirical validation, and ethical issues about data privacy and algorithmic bias in the published papers. Some research lacks clarity on AI methodology, dataset specifications, and consensus processes, stressing the importance of more complete, transparent, and ethically based ways to successfully use ML and Blockchain Technologies in healthcare.

6.4.2 Deep learning-enabled blockchain technologies in healthcare

The use of DL-enabled Blockchain Technologies in healthcare applications, particularly private blockchains like CNN and DNN, is prevalent. However, limited disclosure of consensus algorithms and smart contract implementations highlights the need for increased transparency. Challenges include dataset size limitations, scalability concerns, and ethical considerations. These observations underscore the evolving landscape of integrating DL and Blockchain in healthcare as shown in Table  9 . It emphasizes the necessity for comprehensive reporting and addressing practical implementation challenges for future advancements.

6.4.3 Federated learning -enabled blockchain technologies in healthcare

The fusion of FL and blockchain represents a significant advancement in addressing critical challenges such as privacy risks and data reliability inherent in collaborative ML frameworks. Qammar et al. ( 2023 ) explore the intersection of Blockchain technology and FL in smart healthcare to tackle challenges in decentralized data environments. Their systematic review synthesizes current literature to show how Blockchain’s decentralized ledger can mitigate privacy risks, unreliable model uploads, and high communication costs inherent in FL systems. Through decentralized coordination, communication, and adopting Blockchain’s distributed consensus mechanisms, the authors proposed solutions that enhance data privacy and reliability in FL implementations for healthcare. The study discusses recent advancements and ongoing challenges that require further research to fully exploit Blockchain’s potential in FL integration. Similarly, Myrzashova et al. ( 2023 ) contribute to this discourse by systematically reviewing the literature on blockchain-enabled FL, emphasizing its potential to decentralize healthcare applications, manage dispersed clinical data, and bolster security and privacy in digital healthcare systems. They highlight blockchain’s role in safeguarding sensitive medical information and optimizing collaborative machine learning across distributed networks. Their analysis supports blockchain adoption to address and meet evolving demands for secure and privacy-preserving healthcare data management and analysis.

Furthermore, Zhu et al. ( 2023 ) conducted a systematic survey investigating the integration of blockchain technology with FL to address challenges inherent in decentralized machine learning environments. Their study classifies current integration models into three main architectures namely coupled, decoupled, and overlapped, systematically evaluating their impacts on scalability, data privacy, and security within FL systems. The survey underscores blockchain’s potential to enhance FL by leveraging its decentralized and immutable ledger, effectively mitigating vulnerabilities associated with centralized coordination and ensuring transparent consensus mechanisms. Technical challenges such as blockchain network scalability, optimization of consensus algorithms, and seamless interoperability with FL frameworks are meticulously analyzed. The study contributes by proposing innovative solutions and outlining future research directions aimed at advancing blockchain-enabled FL systems across diverse applications.

Figure  21 illustrates the integrated architecture of FL with blockchain technology. FL participants begin by receiving an initial model and updates to the model based on their respective datasets. These updates are transmitted to miners via APIs like REST, JSON-RPC and gRPC. Miners, serving as nodes in the blockchain network, validate and aggregate these updates using consensus algorithms such as (PoW or PoS). Smart contracts enforce transparent agreements among participants, managing tasks like registration, coordination of training, and reward distribution. Verified blocks containing aggregated updates are appended to the blockchain, allowing FL clients to securely access and download the finalized global model. This architecture enhances collaboration and transparency in FL processes, leveraging blockchain’s decentralized nature for improved model training across distributed participants.

Architecture integrating federated learning with blockchain technologies. gRPC gRPC Remote Procedure Call, RESTful API Representational State Transfer (REST), JSON-RPC JavaScript Object Notation Remote Procedure Call

6.4.4 Heterogeneous federated learning and isomerism learning with blockchain

In the realm of collaborative ML, Heterogeneous Federated Learning (HFL) addresses the diversity of devices and data (Yu et al. 2020 ). Emphasizing the integration of HFL with blockchain, particularly through methodologies like Isomerism Learning, holds considerable potential for enhancing research quality.

6.4.4.1 Isomerism learning, blockchain, and artificial intelligence

Isomerism learning harmonizes FL’s adaptive characteristics with blockchain’s security features, paving the way for robust and privacy-preserving collaborative ML frameworks (Aich et al. 2021 ; Abou El Houda et al. 2023 ). By incorporating Isomerism Learning into the research framework, the study can capitalize on intelligent collaboration among entities to dynamically tackle time-varying challenges in decentralized collaborative learning. Hao et al. ( 2023 ) introduced an innovative approach to analyzing public sentiment in social media, considering various factors such as geography, politics, and ideology. Their study emphasizes advancements in model architecture, incorporating techniques like embedding tables and gating mechanisms to enhance performance. Furthermore, the authors proposed isomerism learning, a new distributed deep learning model that utilizes blockchain technology for secure collaboration and parallel training across distributed nodes. This method dynamically adjusts model weights based on event objectivity, leading to notable improvements in experimental performance evaluations. The integration of blockchain ensures secure data sharing and enhances scalability in collaborative learning environments, highlighting its potential to reshape decentralized ML methodologies.

Biscotti, introduced by Shayan et al. ( 2020 ) is a decentralized peer-to-peer (P2P) scheme based on blockchain technology. It emphasizes maintaining privacy and security among FL peers by securely exchanging secrets using verifiable random functions (VRFs). The framework addresses challenges such as Sybil attacks and data poisoning by authenticating and verifying each peer’s contributions within the decentralized network. Biscotti utilizes a blockchain-inspired approach with off-chain storage and adopts a private blockchain model organized around Merkel trees. It also features a consensus mechanism optimized for integration with Hyperledger Fabric, enhancing resilience and confidentiality in collaborative ML environments.

BlocFL, introduced by Lei et al. ( 2023 ), merges FL with blockchain by replacing the central server with a consortium blockchain. This enables local training of multiple neural networks across various nodes. Researchers have proposed a blockchain-based client selection method to optimize resource allocation, particularly with nodes of varied capabilities. The method showed similar accuracy to the baseline and significantly improved resource utilization, enhancing system scalability and efficiency. In contrast, GCFL (Graph with Coordinator FL), as presented by Ying et al. ( 2023 ), merges FL with a DAG blockchain to minimize data redundancy and facilitate seamless data exchange across devices. GCFL introduces a Two-Phase tips selection consensus algorithm, which significantly reduces resource usage and enhances stability compared to traditional FL systems. This innovative approach effectively improves FL performance while ensuring data privacy and scalability. It surpasses the limitations of single chain blockchains and conventional consensus methods, providing a robust solution for the evolving needs of decentralized ML. Institutional and healthcare collaboration (IFL) occurs securely by sharing model updates on the blockchain, enabling accurate predictions from diverse data. Isomerism Learning adapts the global model to various data distributions, ensuring accuracy. overcoming challenges like scalability, energy consumption, and algorithmic complexities is crucial for fully integrating IFL with blockchain. Nguyen et al. ( 2021a , b ) and Lu et al. ( 2019 ) have highlighted these challenges, emphasizing the need for ongoing research endeavours to refine methodologies, optimize system architectures, and develop novel algorithms. Advancing the integration of HFL with blockchain, particularly through approaches like Isomerism Learning, holds promise for fostering innovation and enabling transformative applications across various domains.

Table 10 summarizes key findings on integrating FL-enabled Blockchain Technologies in healthcare, focusing on COVID-19 identification, fraud detection, and real-time patient monitoring. It highlights Consortium and private blockchains, local gradients, and FL algorithms used. Challenges include scalability and privacy safeguards, emphasizing ongoing refinement needed. Therefore, FL within the tripod framework of AI, healthcare, and blockchain technologies exemplifies a transformative approach in collaborative ML. By integrating FL with blockchain, FL ensures data privacy, enhances collaboration, and drives innovation. As technological advancements continue and regulatory frameworks evolve, FL is poised to revolutionize ML applications, offering economic benefits and advancing data-driven solutions globally.

6.4.5 Machine learning and deep learning -enabled blockchain technologies in healthcare

Table 11 presents an overview of research efforts combining ML and DL with blockchain technologies in healthcare applications. Notably, we found that the research includes a broad range of applications from 2020 to 2023, including securing patient health records, clinical information systems, COVID-19 detection, nutritional recommendations, and illness categorization. While many ML and DL approaches are used, such as DNN, CNN, and transfer learning, the common drawbacks across research include a lack of understanding of blockchain implementation specifications, consensus algorithms, and smart contract details. Concerns concerning suggested frameworks' generalizability, scalability, and real-world application appear as recurring themes. The fusion of AI and blockchain can improve healthcare systems, but further study and precise reporting are required to solve present limits and ensure success.

6.4.6 Deep learning and federated learning-enabled blockchain technologies in healthcare

Table 12 provides a thorough analysis of the usage of DL and FL with blockchain technology in the healthcare field. The research, spanning 2020–2023, will focus on COVID-19 detection, illness prediction, and healthcare model efficiency. Common DL approaches include CNN, ResNet, and CapsNets, while FL methods aim to protect privacy in IoMT and tailored systems. Significant contributions include enhanced data privacy, secure medical imaging diagnostics, and enhanced ML data security. While stressing data protection and easy integration into the IoMTs, research on blockchain implementations frequently lacks definition, leaving critical information on types, smart contracts, and consensus methods unknown. Common restrictions include real-world implementation obstacles, legal frameworks, and scalability issues. Clear analysis of blockchain features is critical for establishing successful and scalable healthcare solutions in this changing context.

6.4.7 Reinforcement learning-enabled blockchain technologies in healthcare

The research explores the fusion of Reinforcement Learning (RL) with Blockchain technology in the healthcare industry as shown in Table  13 , focusing on applications like EHRs, COVID-19 services, and safe data offloading. It uses multi-agent systems and deep reinforcement learning algorithms to improve data protection, secure healthcare services, and optimize medical data offloading. However, uncertainties, lack of AI approach specifics, and insufficient knowledge of Blockchain technology are common issues. More research is needed to improve RL and Blockchain integration in healthcare.

6.4.8 Explainable AI-enabled blockchain technologies in healthcare

Table 14 provides an overview of Explainable Artificial Intelligence (XAI) integrated with Blockchain Technologies in healthcare applications. The study from 2023 focuses on smart and secure healthcare facilities using XAI methods like Grad CAM/LIME. This trend aligns with the AI community’s focus on transparency and interpretability. XAI’s usage in healthcare is expected to grow, indicating a shift towards more accountable and understandable AI systems. However, the study highlights ongoing challenges and the potential for continued advancements in this dynamic field.

6.5 Fusion of AI-enabled blockchain performance metrics in healthcare

Table 15 provides a thorough overview of research publications examining the convergence of AI and blockchain technology in healthcare, displaying a broad spectrum of applications and addressing important performance criteria year-wise. The research evaluates AI metrics like accuracy and AUC, along with blockchain measures like security and privacy. It draws attention to concerns about security and confidentiality, as well as the use of permissioned and public blockchains and the investigation of cutting-edge technology like NFTs for secure medical diagnostics. Many studies emphasize the need for real-time scalability, FL, interpretability, and energy efficiency. Blockchain plays an important role in ensuring trust, accountability, and trust. AI-Blockchain integration is revolutionizing global collaboration in medical learning processes, reshaping healthcare operations like a future Tripod.

7 Critical discussion

7.1 comparative and comprehensive analysis.

This narrative review offers a thorough overview of blockchain-based AI healthcare applications considering articles published between 2005 and 2023. It covers various healthcare domains, including infectious disease, COVID-19, e-Health, EHR/PHR management systems, and healthcare supply chains. The review explores advanced solutions to Blockchain-based healthcare challenges using AI-powered techniques. It examines the interdependence of various AI methodologies, including Deep Learning (DL), Machine Learning (ML), Reinforcement Learning (RL), Federated Learning (FL), and Explainable AI (XAI). The review explores the potential of the integration of Blockchain in AI healthcare, highlighting its multifaceted impact and potential. It serves as a point of reference for various applications, emphasizing the growing integration of Blockchain in AI healthcare to ensure data privacy and security, establishing it as a resilient tripod for the future.

7.2 Major findings

Derived from the integration and implementation of AI and Blockchain technology in the medical sector, we have drawn certain findings in our review. We have arrived at the following main findings based on these observations and challenges, which are covered in Sects.  2 , 3 , 4 , 5 , and 6 :

Applications: The features of Blockchain, smart contracts, security, and privacy are widely addressed in a variety of applications such as securing EHRs, combating COVID-19, and e-health. This is shown in Table  5 . Various types of blockchains exist, with public and private blockchains emerging as the predominant choices for healthcare applications.

Blockchain architecture and its working : although many papers have been written on the integration of Blockchain into healthcare applications, none have described how Blockchain architecture works and how Blockchain technologies, such as Ethereum and Hyperledger, connect with healthcare applications. Figure  11 depicts the detailed architecture of layers with its examples.

Blockchain solution for healthcare applications: Current Blockchain technology issues that might jeopardize the system’s fundamental nature and gave recent efficient Blockchain solutions, demonstrating this technology a dependable answer to all Blockchain-based healthcare applications. It is presented in Table  2 .

Data challenges in healthcare AI: leveraging blockchain solutions:

In healthcare AI, data collection is a significant challenge. AI model training demands extensive, unbiased, high-quality datasets for effective development. Recent healthcare applications increasingly leverage Blockchain in Sect. 6.2 for securing data transmission and records, emphasizing its role in overcoming data challenges.

Decentralized computing using blockchain and AI: Blockchain and AI interdependence can help both the technologies with decentralized computing and scalable Blockchains. The detailed dependency is given clearly in Table  4 .

AI in healthcare challenges and blockchain solutions: Table  3 under Sect.  4 highlights challenges in real-time AI implementation in healthcare, with data privacy and security at the forefront. Blockchain emerges as a robust solution, enhancing the diagnostic process by ensuring heightened privacy and security.

Power of privacy and security in tripod design: the power of Tripod is dependent on the many AI-enabled Blockchain features, especially privacy, and security, which are essential for speeding up the deployment of data-specific AI applications. From Table  15 it is evident that the most widely used metrics in implemented integration papers of AI and blockchain are security and privacy in addition to AI metrics such as accuracy and AUC.

AI and blockchain in healthcare applications: Fig.  6 shows that AI and Blockchain are primarily used in healthcare applications for securing EHRs and COVID-19 data transmission, accounting for 39% and 30% of published articles from 2017 to 2023.

Validation: The most prominent AI assessment metrics for the research under consideration are accuracy and performance, showing that it relies on reliable and unbiased data, requiring Blockchain’s involvement. This proves that our idea of combining Blockchain with AI may enhance healthcare organizations, making our tripod dependable and efficient.

Rise in fusion of AI and blockchain publications in healthcare: Our first claim from Fig.  3 a illustrates that there is a substantial rise in the publication of articles from 2017 to 2023, ranging from 1 to 24 articles, underscores the increasing recognition of AI adopting blockchain in healthcare. This trend affirms the pivotal role of this fusion as a potent tripod shaping the future landscape of healthcare research and applications.

Use of AI techniques and rise of FL and DL in healthcare: Our first claim from Fig.  5 a is proof that the adoption of FL and the combination of FL and DL started rising in the healthcare industry too, and it can be used in future research because holds the capability to address limitations inherent in methodologies dependent on a singular centralized data pool. This, therefore, allows us to be able to provide large-scale precision medicine, leading to models that give unbiased judgments. Figure

Use of blockchain type and rise of private blockchain in healthcare: Fig.  4 a, shows the proof that private blockchain is widely used in the healthcare industry, with 20 of the 51 AI and blockchain fusion implemented articles utilizing this type of application. However, 14 articles selected public blockchain, demonstrating the general inclination toward private blockchain when integrating AI in healthcare.

Implementation of AI and blockchain in healthcare: We found that though Blockchain is mostly adopted in integration with AI in selected articles, most of the articles just mention their usage without detailing their implementation. Figure  3 b presents proof that the number of articles not implemented is 28 out of the total 79 articles published. However, Fig.  3 c indicates a positive trend, showing a rise in practical implementation from 2020 onwards.

7.4 A special note on datasets

The narrative evaluation requires a specific mention of the sorts of datasets used, particularly device-based datasets. Numerous prominent sensor-based datasets have been developed by researchers. Table 16 outlines the different datasets used in healthcare with Blockchain and AI integration with attributes such as (i) data source, (ii) dataset quantity employed for the study, (iii) dataset magnitude, (iv) device used, and (v) type of data.

This research explores the use of various datasets in various applications, including blockchain applications in EHR record sharing, real-time data, and X-ray COVID-19 data categorization. The studies examined in this research employed diverse datasets, with some relying on a single dataset and others utilizing multiple datasets.

In 2021, X-ray COVID-19 and COCO played crucial roles in COVID-19 screening and social distancing. In 2022, ToN-IoT/IoT-Botnet and MNIST/CIFAR-10 (Jennath et al. 2020 ; Kim and Huh 2020 ) datasets focused on secure data exchange and privacy-preserving healthcare. In 2023, CDC data, Medical MNIST/Tissue MNIST/OCTMNIST (Bhattacharya et al. 2019 ; Połap et al. 2020 ), and Monkeypox addressed COVID-19 detection, healthcare efficiency, and patient privacy issues. MNIST, CIFAR-10, CC-19/COVID-19-CT, MIMIC-III (Johnson et al. 2016 ), and 2-D Colon Pathology/Breast Tumor datasets (Fleurence et al. 2014 ), were frequently used, demonstrating their adaptability in applications ranging from privacy protection to infectious disease diagnostics (Kumar et al. 2021a , b ). Tuberculosis Chest X-ray Image Data Sets Heart Disease UCI (Jaeger et al. 2014 ). All this data requires security and privacy while sharing or gathering, and Blockchain was used to accomplish this. Some datasets are publicly available, requiring minimal prior approval due to confidentiality. Devices for data collection include CCTV cameras, smartphones, smart wearable devices, CT scanners, hospitals, patients, and edge nodes. Blockchain was employed to ensure the security and privacy of sharing and gathering these data.

8 Future work plan

Expanding advanced features of AI and their potential interfacing with Blockchain in the future.

Pruning for artificial intelligence storage reduction: Pruning involves removing unnecessary nodes or filters from deep learning (DL) models to minimize their size and enhance their prediction time (Xu et al. 2019 ). If the prediction time decreases, the DL models will be suitable for usage in edge devices, particularly in health care (Oguntola et al. 2018 ). The storage requirements of the DL model are the major focus of the pruning process, and its final goal is to make the model more storage-friendly (Choudhary et al. 2020 ). Weight pruning (Han et al. 2015 ), neuron pruning (Srinivas and Babu 2015 ; Skandha et al. 2022a , b ), filter pruning for CNN (Li et al. 2016 ), and layer pruning (Chen and Zhao 2018 ) are a few pruning techniques. There has yet to be seen AI application with Blockchain using pruning strategies.

Explainable artificial intelligence (XAI): Explainable AI helps to comprehend and interpret machine learning model predictions, and using it can debug, enhance, and explain model behavior completely (Chalkiadakis 2018 ; Linardatos et al. 2020 ). In healthcare, clinicians must comprehend the functionality and workings of black-box models, including their decision-making processes, to validate the reasoning behind the model’s conclusions (Tjoa and Guan 2020 ; Zhang et al. 2022 ). The recent adoption of transparent AI-powered systems indicates a promising future trajectory towards fully explainable AI, creating opportunities for further developments in XAI.

8.1 Strengths, weakness, and extensions

This review is a trailblazing attempt that presents and explores the integration of blockchain and AI in various healthcare applications, including COVID-19 and EHR/PHR, providing a comprehensive overview of the field. Emphasized existing blockchain technology issues that might ruin the system’s core nature and presented recent efficient blockchain solutions hence proving this technology’s reliability for Blockchain-based AI healthcare applications. We have discussed the blockchain architecture which can give an understanding of blockchain frameworks integrated with healthcare applications. Thorough and comparative research was conducted throughout the benchmarking phase. A significant amount of work was done in this healthcare study using the fusion of Blockchain and AI, but some limitations and drawbacks need to be addressed .

Each of these proposed frameworks and architectures has its benefits, but none has yet used blockchain technology to help AI be used in a decentralized and fully distributed way in healthcare systems.

Many articles have developed and addressed AI implants, but few have covered the full implementation of the Blockchain architecture. Blockchain technology is being applied in the healthcare sector, where special issues such as security must be managed.

The main issue associated with this modern technology in medical institutions is a lack of knowledge and implementation cost.

There are several aspects which can be considered for the future. Note that the outcomes of AI are mostly determined by the data, however, there are several key concerns, such as user privacy and security. The answer to these issues is Blockchain distributed architecture with transparent data. The public Blockchain and FL combination has been one of the most popular ways in the previous three years. Finally, the use of FL and DL in private Blockchain framework has shown remarkable results in healthcare. But there is truly little work done on DRL and consortium Blockchain, which can be the next revolution in the healthcare domain.

9 Ethical implications and societal impacts of AI and blockchain in healthcare

The fusion of blockchain and AI in healthcare is promising for improved efficiency and patient care. However, ethical, and societal impacts must be carefully examined to ensure (i) responsible deployment, (ii) safeguard patient rights, and (iii) promote equitable access to quality care.

9.1 Ethical implications

9.1.1 algorithmic bias and fairness.

The fusion of blockchain with AI algorithms in healthcare raises ethical concerns about algorithmic fairness and bias. Blockchain’s transparency in data transactions is pivotal, yet ensuring AI algorithms provide equitable treatment to all patient groups remains essential. Bias is important to study when it comes to AI in healthcare (Suri et al. 2021a , b ; Das et al. 2022 ; Paul et al. 2022 ). Ethical guidelines are necessary to mitigate biases, promote fairness, and prevent discriminatory outcomes in healthcare decision-making. In the healthcare landscape, data’s pivotal role underscores concern regarding the bias in acquiring accurate data for training AI models (Estiri et al. 2022 ). AI bias encompasses data or algorithmic bias and societal AI bias. Algorithmic bias results from biases in training data or algorithm design, potentially leading to disparities in healthcare services (Panch et al. 2019 ; Kumar et al. 2024 ). Societal AI bias reflects societal intolerance or institutional prejudice (Norori et al. 2021 ). Addressing bias in healthcare AI involves proper feature extraction, crucial for mitigating bias, and effective performance assessment methods (FitzGerald and Hurst 2017 ).

Employing appropriate techniques and methods helps minimize bias in healthcare AI applications, ensuring fair and equitable healthcare delivery (Suri et al. 2021a , b , 2022a ). Moreover, by using augmentation methods like the Adaptive Synthetic Sampling Method (ADASYN) and (Synthetic Minority Over-sampling Technique) SMOTE, the balanced data architecture may reduce bias brought on by unbalanced data (Alex et al. 2024 ). By creating artificial data points for the minority class, these techniques even out the distribution of classes within the dataset. By deliberately employing these augmentation techniques, AI systems may learn from a more balanced representation, lessening the influence of bias.

9.1.2 Transparency and accountability

Blockchain technology serves as a powerful tool in fostering transparency and accountability in the utilization of AI algorithms (Zhou et al. 2024 ). The unalterable ledger of blockchain assures that every transaction and activity carried out by AI algorithms is forever recorded and cannot be changed (Wang et al. 2019a , b ). Blockchain enhances accountability, openness, and fairness in decision-making processes by providing a complete record of AI activity, enabling stakeholders to evaluate and analyze data to detect and prevent bias in AI systems (Lo et al. 2022 ). The system’s decentralized nature minimizes centralized manipulation and promotes fairness by distributing decision-making data across multiple nodes for transparent recording.

9.1.3 Automation with smart contracts

Smart contracts automate the enforcement of established rules and criteria without the need for human interaction, hence preventing bias (Omar et al. 2021 ). They use fairness principles and ethical rules to detect and rectify biases, eliminating subjective human judgment and reducing the likelihood of biased decisions. Blockchain’s Audibility features enable comprehensive auditing, tracing biased decisions back to their source. A study by Bose et al. ( 2024 ), explored addressing bias using blockchain smart contracts and the results show that blockchain contracts help maintain accurate data, which is crucial for reducing bias. Overall, blockchain enhances transparency, accountability, and audibility in AI decision-making.

9.1.4 Data ownership and patient consent

It is critical in both AI and blockchain implementations to guarantee that patients own their health data and are fully informed about its collection, storage, and use. Blockchain technology presents a unique opportunity to improve data ownership and patient consent management (Mann et al. 2021 ). Patients may take control of their health data by managing access rights, keeping an eye on data consumption, and adopting blockchain-based decentralized platforms (Jabarulla and Lee 2021a , b ). Moreover, clear standards for the collection, storage, and usage of data should be established, and patients should be informed about their data usage. Sophisticated permission management systems that enable patients to quickly grant, cancel, or monitor their consent for the use of their data while maintaining their autonomy can be provided using blockchain-connected AI systems (Tith et al. 2020 ).

9.1.5 Equity in access to healthcare services

The integration of AI and blockchain technology in healthcare has the potential to greatly improve equity in access to advanced healthcare services by decentralizing them, decreasing inequities, and increasing transparency and trust (Till et al. 2017 ). AI-powered telemedicine can provide high-quality treatment to rural and underprivileged populations, while blockchain assures safe, transparent data sharing and empowers patients with access to their health data (Abugabah et al. 2020 ). Personalized treatment plans and AI-optimized resource allocation guarantee that varied populations receive individualized care (Frank and Olaoye 2024 ). Addressing digital gaps, algorithmic bias, and data privacy is crucial for ethically implementing AI technologies in healthcare. Inclusive development, robust infrastructure, and continuous monitoring of AI algorithms are essential for achieving fair healthcare outcomes. Additionally, investing in digital infrastructure and providing education can help bridge the digital divide, ensuring that all patients can benefit from the advancements in AI and blockchain healthcare technologies (Ragnedda and Destefanis 2019 ).

9.1.6 Data privacy and security

The integration of AI and blockchain technologies requires robust measures to protect patient confidentiality and prevent unauthorized access to sensitive healthcare information, ensuring patient data privacy and security (Zhang et al. 2021a , b ). The implementation of encryption techniques, strict access control policies, and secure data storage on blockchain networks are crucial for ensuring privacy and security (Kumar et al. 2022a , b , c , d ). Regular audits, data minimization practices, and employee training are essential for mitigating risks. Compliance with regulations like HIPAA and GDPR is crucial for upholding patient rights and privacy (Bakare et al. 2024 ). These measures protect patient data confidentiality and maintain trust in AI and blockchain technologies.

9.2 Societal impacts

9.2.1 enhanced healthcare accessibility and equity.

The fusion of AI and blockchain technology can bridge regional barriers and eliminate healthcare inequities by enabling telemedicine, remote consultations, decentralized networks, AI-driven resource allocation, and personalized healthcare solutions (Guo et al. 2019 ). This democratizes access to high-quality services, ensuring everyone, regardless of location or socioeconomic status, has access to necessary treatments and interventions (Yang et al. 2020 ).

9.2.2 Empowerment of patients barriers and healthcare consumers

The empowerment of patients and healthcare consumers is one important social impact. Blockchain technology provides healthcare consumers with transparent access to their health information and decision-making processes, empowering patients, and consumers (Hang, Kim et al. 2021). Patients are now more equipped to design individualized treatment plans and make informed decisions. Increased satisfaction with care and better health outcomes are likely to be experienced by patients due to the shift toward patient-centered care (Abbas et al. 2020 ).

9.2.3 Transformation of healthcare workforce and skills

This fusion of AI and blockchain technology transforms the healthcare workforce and skill sets (Sousa and Rocha 2019 ). Healthcare personnel must adapt to new technologies and workflows made by AI automation and blockchain data management solutions. This transition includes initiatives to upskill and reskill healthcare workers so that they can effectively employ new technologies to offer high-quality care. Furthermore, new roles such as AI specialists, blockchain developers, and data analysts arise, transforming the healthcare workforce and creating new opportunities.

9.2.3.1 Ethical and regulatory considerations

Adhering to strict regulations on data protection, security, and compliance creates challenges for deploying AI and blockchain technology in healthcare (Al Meslamani 2023 ). It has significant societal implications, including protecting health data privacy and security, preventing bias in AI algorithms, and upholding accountability in AI-driven decision-making (Chen et al. 2018 ; Stephanie et al. 2023 ). To address these challenges, regulatory frameworks must change, including creating guidelines for platform interoperability and data exchange and ensuring adherence to data protection regulations like GDPR (Shuaib et al. 2021 ).

9.2.3.2 Economic implications

There are significant financial implications when using blockchain and AI in healthcare. These technologies have the power to improve resource allocation, save costs, and streamline healthcare delivery processes (Munoz et al. 2019 ). They also promote entrepreneurship and innovation in the healthcare industry, which leads to the creation of new goods, services, and business ideas (Pergher et al. 2016 ). Additionally, the development of jobs and economic growth and prosperity are facilitated by investments in blockchain and AI technologies.

9.2.3.3 Cultural and social change

The widespread use of AI and blockchain in healthcare is transforming the industry, fostering innovation and cooperation among practitioners, researchers, and technology (Zhang et al. 2021 ). It also promotes patient involvement in decision-making and democratizes healthcare information, empowering individuals to take control of their health and well-being (Jabarulla and Lee 2021a , b ). However, realizing the complete capabilities of these technologies necessitates addressing ethical, regulatory, and cultural considerations to ensure their deployment in a socially accountable and equitable manner.

10 Conclusion

In conclusion, the fusion of blockchain and AI technologies presents a transformative opportunity to revolutionize the healthcare sector. Our analysis underscores the growing adoption of blockchain for securing EHRs, enhancing data privacy, and facilitating secure data transmission in healthcare. Concurrently, the rise of federated learning and deep learning techniques highlights AI’s potential to drive precision medicine and personalized healthcare solutions. The preference for private blockchain implementation underscores the healthcare sector’s commitment to data security and privacy, reflecting a broader shift towards real-world integration and deployment of AI and blockchain technologies.

Blockchain’s ability to secure data transfer complements AI’s need for secure storage, facilitating increased data security, improved service efficiency, and enhanced accessibility in healthcare. By granting healthcare stakeholders access to patient medical records on the blockchain, this fusion can streamline procedures, potentially saving billions for the industry while ensuring enhanced security, privacy, and accessibility. The convergence of blockchain and AI offers solutions across healthcare domains, including infectious diseases, COVID-19, e-Health, and EHR/PHR management, with the potential to significantly enhance disease identification, response, and overall healthcare efficacy. The future tripod of blockchain, AI, and healthcare promises to improve patient outcomes, and address key challenges in the healthcare sector.

Looking ahead, exploring advanced AI features such as pruning and explainable AI (XAI) integrated with blockchain holds promise for further enhancing healthcare outcomes. As these technologies evolve, their collaborative potential will continue to shape the future of healthcare delivery worldwide, guaranteeing data security and privacy while driving unprecedented innovation and efficiency.

Data availability

No datasets were generated or analysed during the current study.

Abbas K, Afaq M, Ahmed Khan T, Song W-C (2020) A blockchain and machine learning-based drug supply chain management and recommendation system for smart pharmaceutical industry. Electronics 9(5):852. https://doi.org/10.3390/electronics9050852

Article   Google Scholar  

Abbasian Ardakani A, Acharya UR, Habibollahi S, Mohammadi A (2021) COVIDiag: a clinical CAD system to diagnose COVID-19 pneumonia based on CT findings. Eur Radiol 31:121–130. https://doi.org/10.1007/s00330-020-07087-y

Abou El Houda Z, Hafid AS, Khoukhi L, Brik B (2023) When Collaborative Federated Learning Meets Blockchain to Preserve Privacy in Healthcare. IEEE Trans Netwk Sci Eng. https://doi.org/10.1109/TNSE.2022.3211192

Abu-Elezz I, Hassan A, Nazeemudeen A, Househ M, Abd-Alrazaq A (2020) The benefits and threats of blockchain technology in healthcare: a scoping review. Int J Med Informatics 142:104246. https://doi.org/10.1016/j.ijmedinf.2020.104246

Abugabah A, Nizam N, Alzubi AA (2020) Decentralized telemedicine framework for a smart healthcare ecosystem. IEEE Access 8:166575–166588. https://doi.org/10.1109/ACCESS.2020.3021823

Acharya RU et al (2012) Sree SV Molinari F Saba L Nicolaides A Suri JS Symptomatic vs. asymptomatic plaque classification in carotid ultrasound. J Med Syst 36(3):1861–1871. https://doi.org/10.1007/s10916-010-9645-2

Acharya U, Vinitha Sree S, Mookiah M, Yantri R, Molinari F, Zieleźnik W, Małyszek-Tumidajewicz J, Stępień B, Bardales R, Witkowska A (2013a) Diagnosis of Hashimoto’s thyroiditis in ultrasound using tissue characterization and pixel classification. Proc Inst Mech Eng [h] 227(7):788–798. https://doi.org/10.1177/0954411913483637

Acharya UR, Faust O, Kadri NA, Suri JS, Yu W (2013b) Automated identification of normal and diabetes heart rate signals using nonlinear measures. Comput Biol Med 43(10):1523–1529. https://doi.org/10.1016/j.compbiomed.2013.05.024

Acharya UR, Sree SV, Saba L, Molinari F, Guerriero S, Suri JS (2013d) Ovarian tumor characterization and classification using ultrasound—a new online paradigm. J Digit Imaging 26(3):544–553. https://doi.org/10.1007/s10278-012-9553-8

Acharya UR, Sree SV, Kulshreshtha S, Molinari F, Koh JEW, Saba L, Suri JS (2014) GyneScan: an improved online paradigm for screening of ovarian cancer via tissue characterization. Technol Cancer Res Treat 13(6):529–539. https://doi.org/10.7785/tcrtexpress.2013.600273

Afshar P, Heidarian S, Naderkhani F, Oikonomou A, Plataniotis KN, Mohammadi A (2020) Covid-caps: a capsule network-based framework for identification of covid-19 cases from x-ray images. Pattern Recogn Lett 138:638–643. https://doi.org/10.1016/j.patrec.2020.09.010

Agarwal M, Saba L, Gupta SK, Carriero A, Falaschi Z, Paschè A, Danna P, El-Baz A, Naidu S, Suri JS (2021a) A novel block imaging technique using nine artificial intelligence models for COVID-19 disease classification, characterization and severity measurement in lung computed tomography scans on an Italian cohort. J Med Syst 45(3):1–30. https://doi.org/10.1007/s10916-021-01707-w

Agarwal M, Saba L, Gupta SK, Johri AM, Khanna NN, Mavrogeni S, Laird JR, Pareek G, Miner M, Sfikakis PP (2021b) Wilson disease tissue classification and characterization using seven artificial intelligence models embedded with 3D optimization paradigm on a weak training brain magnetic resonance imaging datasets: a supercomputer application. Med Biol Eng Compu 59(3):511–533. https://doi.org/10.1007/s11517-021-02322-0

Agarwal M, Agarwal S, Saba L, Chabert GL, Gupta S, Carriero A, Pasche A, Danna P, Mehmedovic A, Faa G (2022) Eight pruning deep learning models for low storage and high-speed COVID-19 computed tomography lung segmentation and heatmap-based lesion localization: a multicenter study using COVLIAS 2.0. Compt Biol Med. https://doi.org/10.1016/j.compbiomed.2022.105571

Aggarwal P, Vig R, Bhadoria S, Dethe C (2011) Role of segmentation in medical imaging: a comparative study. Int J Compt Applicat 29(1):54–61. https://doi.org/10.1109/CCAA.2018.8777561

Aggarwal A, Mittal M, Battineni G (2021) Generative adversarial network: an overview of theory and applications. Int J Info Manag Data Insights 1(1):100004. https://doi.org/10.1016/j.jjimei.2020.100004

Ahmed I, Chehri A, Jeon G (2023) Artificial intelligence and blockchain enabled smart healthcare system for monitoring and detection of COVID-19 in biomedical images. IEEE/ACM Trans Comput Biol Bioinf. https://doi.org/10.1109/TCBB.2023.3294333

Aich S, Sinai NK, Kumar S, Ali M, Choi YR, Joo MI, Kim HC (2021) Protecting personal healthcare record using blockchain & federated learning technologies. In: 2021 23rd international conference on advanced communication technology (ICACT), IEEE. https://doi.org/10.23919/ICACT53585.2022.9728772

Al Meslamani AZ (2023) Technical and regulatory challenges of digital health implementation in developing countries. Taylor Francis 26:1057–1060. https://doi.org/10.1080/13696998.2023.2249757

Alex SA, Nayahi JJV, Kaddoura S (2024) Deep convolutional neural networks with genetic algorithm-based synthetic minority over-sampling technique for improved imbalanced data classification. Appl Soft Comput 156:111491. https://doi.org/10.1016/j.asoc.2024.111491

Alhadhrami Z, Alghfeli S, Alghfeli M, Abedlla JA, Shuaib K (2017) Introducing blockchains for healthcare. 2017 international conference on electrical and computing technologies and applications (ICECTA). IEEE. https://doi.org/10.1109/ICECTA.2017.8252043

Alhazmi HE, Eassa FE, Sandokji SM (2022) Towards big data security framework by leveraging fragmentation and blockchain technology. IEEE Access 10:10768–10782. https://doi.org/10.1109/ACCESS.2022.3144632

Ali S, Abdullah TPT, Armand A, Athar A, Hussain M, Ali M, Yaseen M-I, Kim H-C (2023) Metaverse in healthcare integrated with explainable ai and blockchain: enabling immersiveness, ensuring trust, and providing patient data security. Sensors 23(2):565. https://doi.org/10.3390/s23020565

Allison PD (1999) Multiple regression: a primer. Pine Forge Press, Thousand Oaks

Google Scholar  

Alrubei S, Ball E, Rigelsford J (2021) The use of blockchain to support distributed AI implementation in IoT systems. IEEE Internet Things J. https://doi.org/10.1109/JIOT.2021.3064176

Alruwaili FF (2020) Artificial intelligence and multi agent based distributed ledger system for better privacy and security of electronic healthcare records. PeerJ Comput Sci 6:e323. https://doi.org/10.7717/peerj-cs.323

Alruwaili FF, Alabduallah B, Alqahtani H, Salama AS, Mohammed GP, Alneil AA (2023) Blockchain enabled smart healthcare system using jellyfish search optimization with dual-pathway deep convolutional neural network. IEEE Access. https://doi.org/10.1109/ACCESS.2023.3304269

Al-Safi H, Munilla J, Rahebi J (2022) Patient privacy in smart cities by blockchain technology and feature selection with Harris Hawks Optimization (HHO) algorithm and machine learning. Multimedia Tools Appl 81(6):8719–8743. https://doi.org/10.1007/s11042-022-12164-z

Alzoubi YI, Mishra A (2023) Green blockchain—a move towards sustainability. J Clean Prod 430:139541. https://doi.org/10.1016/j.jclepro.2023.139541

Alzubi JA, Alzubi OA, Singh A, Ramachandran M (2022) Cloud-IIoT-based electronic health record privacy-preserving by CNN and blockchain-enabled federated learning. IEEE Trans Industr Inf 19(1):1080–1087. https://doi.org/10.1109/TII.2022.3189170

Analytics C (2018) EndNote [Software]

Androulaki E, Barger A, Bortnikov V, Cachin C, Christidis K, De Caro A, Enyeart D, Ferris C, Laventman G, Manevich Y (2018) Hyperledger fabric: a distributed operating system for permissioned blockchains. Proc Thirteenth EuroSys Conf. https://doi.org/10.1109/ICCCNT45670.2019.8944615

Anita N, Vijayalakshmi M (2019) Blockchain security attack: a brief survey. 2019 10th international conference on computing, communication and networking technologies (ICCCNT), IEEE. https://doi.org/10.1109/ICCCNT45670.2019.8944615

Antal C, Cioara T, Antal M, Anghel I (2021) Blockchain platform for COVID-19 vaccine supply management. IEEE Open J Comput Soc 2:164–178. https://doi.org/10.1109/OJCS.2021.3067450

Araki T, Kumar PK, Suri HS, Ikeda N, Gupta A, Saba L, Rajan J, Lavra F, Sharma AM, Shafique S (2016) Two automated techniques for carotid lumen diameter measurement: regional versus boundary approaches. J Med Syst 40(7):1–19. https://doi.org/10.1007/s10916-016-0543-0

Archana Bathula SKG, Suresh M, Sanagala SS (2022) Academic projects on certification management using blockchain—a review. international conference on recent trends in microelectronics, automation, computing and communication systems. IEEE. Hyderabad, IEEE. https://doi.org/10.1109/ICMACC54824.2022.10093679

Baaske A, Brotto LA, Galea LA, Albert AY, Smith L, Kaida A, Booth A, Gordon S, Sadarangani M, Racey CS (2022) Barriers to accessing contraception and cervical and breast cancer screening during COVID-19: a prospective cohort study. J Obstet Gynaecol Can 44(10):1076–1083. https://doi.org/10.1016/j.jogc.2022.05.011

Back A, Corallo M, Dashjr L, Friedenbach M, Maxwell G, Miller A, Poelstra A, Timón J, Wuille P (2014) Enabling blockchain innovations with pegged sidechains 72, pp 201–224. http://www.opensciencereview.com/papers/123/enablingblockchain-innovations-with-pegged-sidechains

Baird L, Harmon M, Madsen P (2019) Hedera: a public hashgraph network & Governing Council. White Paper 1

Bakare SS, Adeniyi AO, Akpuokwe CU, Eneh NE (2024) Data privacy laws and compliance: a comparative review of the EU GDPR and USA regulations. Compt Sci IT Res J 5(3):528–543. https://doi.org/10.51594/csitrj.v5i3.859

Balasubramanian S, Shukla V, Sethi JS, Islam N, Saloum R (2021) A readiness assessment framework for Blockchain adoption: a healthcare case study. Technol Forecast Soc Chang 165:120536. https://doi.org/10.1016/j.techfore.2020.120536

Balasubramanian R (2022) Region-based convolutional neural network (RCNN). línea]. https://medium.com/analytics-vidhya/region-based-convolutionalneural-network-rcnn-b68ada0db871 . [Último acceso: Septiembre 2021]

Banchhor SK, Londhe ND, Araki T, Saba L, Radeva P, Laird JR, Suri JS (2017) Wall-based measurement features provides an improved IVUS coronary artery risk assessment when fused with plaque texture-based features during machine learning paradigm. Comput Biol Med 91:198–212. https://doi.org/10.1016/j.compbiomed.2017.10.019

Bancilhon F, Kim W, Korth HF (1985) A model of CAD transactions, University of Texas at Austin, Department of Computer Sciences

Banerjee DN, Chanda SS (2020) AI failures: a review of underlying issues. https://arxiv.org/abs/2008.04073 . https://doi.org/10.48550/arXiv.2008.04073

Barhamgi M, Bertino E (2022) Special issue on data transparency—data quality, annotation, and provenance. J Data Info Quality (JDIQ) 14(1):1–3. https://doi.org/10.1145/3494454

Bathula A, Muhuri S, GuptaMerugu SKS (2022a) Secure certificate sharing based on blockchain framework for online education. Multimedia Tools Appl 82(11):16479–16500. https://doi.org/10.1007/s11042-022-14126-x

Bathula A, Muhuri S, Merugu S, Gupta SK (2022) Designing framework for intrusion detection in IoT based on spotted hyena-based ANN. ICDSMLA 2020, Springer. pp 1615–1629. https://doi.org/10.1007/978-981-16-3690-5_153

Baucas MJ, Spachos P, Plataniotis KN (2023) Federated learning and blockchain-enabled fog-IoT platform for wearables in predictive healthcare. IEEE Trans Comput Soc Syst. https://doi.org/10.48550/arXiv.2301.04511

Belchior R, Somogyvari P, Pfannschmidt J, Vasconcelos A, Correia M (2023) Hephaestus: modeling, analysis, and performance evaluation of cross-chain transactions. IEEE Trans Reliab

Bengio Y, LeCun Y (2007) Scaling learning algorithms towards AI. Large-Scale Kernel Mach 34(5):1–41. https://doi.org/10.1109/TR.2023.3336246

Benji M, Sindhu M (2019) A study on the Corda and Ripple blockchain platforms. Advances in big data and cloud computing, Springer. pp 179–187 https://doi.org/10.1007/978-981-13-1882-5_16

Bera B, Das AK, Obaidat M, Vijayakumar P, Hsiao K-F, Park Y (2020) AI-enabled blockchain-based access control for malicious attacks detection and mitigation in IoE. IEEE Consum Electron Mag. https://doi.org/10.1109/MCE.2021.3137104

Bera B, Mitra A, Das AK, Puthal D, Park Y (2021) Private blockchain-based AI-envisioned home monitoring framework in IoMT-enabled COVID-19 environment. IEEE Consum Electron Mag. https://doi.org/10.1109/MCE.2021.3137104

Bhattacharya P, Tanwar S, Bodke U, Tyagi S, Kumar N (2019) Bindaas: blockchain-based deep-learning as-a-service in healthcare 4.0 applications. IEEE Trans Netw Sci Eng. https://doi.org/10.1109/TNSE.2019.2961932

Biswas M, Kuppili V, Edla DR, Suri HS, Saba L, Marinhoe RT, Sanches JM, Suri JS (2018) Symtosis: a liver ultrasound tissue characterization and risk stratification in optimized deep learning paradigm. Comput Methods Programs Biomed 155:165–177. https://doi.org/10.1016/j.cmpb.2017.12.016

Böck M, Malle J, Pasterk D, Kukina H, Hasani R, Heitzinger C (2022) Superhuman performance on sepsis MIMIC-III data by distributional reinforcement learning. PLoS ONE 17(11):e0275358. https://doi.org/10.1371/journal.pone.0275358

Bongini P, Bianchini M, Scarselli F (2021) Molecular generative graph neural networks for drug discovery. Neurocomputing 450:242–252. https://doi.org/10.1016/j.neucom.2021.04.039

Bose A, Sarkar P, Jana P (2024) Data biasing removal with blockchain and crowd annotation. Procedia Compt Sci 233:692–702. https://doi.org/10.1016/j.procs.2024.03.258

Bouman CA, Shapiro M, Cook G, Atkins CB, Cheng H (1997) Cluster: an unsupervised algorithm for modeling Gaussian mixtures

Bushaj S, Yin X, Beqiri A, Andrews D, Büyüktahtakın İE (2022) A simulation-deep reinforcement learning (SiRL) approach for epidemic control optimization. Annals Operat Res. https://doi.org/10.1007/s10479-022-04926-7

Buterin V, Illum J, Nadler M, Schär F, Soleimani A (2024) Blockchain privacy and regulatory compliance: towards a practical equilibrium. Blockchain Res Appl 5(1):100176. https://doi.org/10.1016/j.bcra.2023.100176

Castelli M, Vanneschi L, Largo ÁR (2018) Supervised learning: classification. Por Ranganathan, S., M. Grisbskov, K. Nakai y C. Schönbach 1:342–349. https://doi.org/10.1016/B978-0-12-809633-8-.20332-4

Castellino RA (2005) Computer aided detection (CAD): an overview. Cancer Imaging 5(1):17. https://doi.org/10.1102/1470-7330.2005.0018

Castro, M. and B. Liskov (1999). Practical byzantine fault tolerance. OSDI

Celesti A, Ruggeri A, Fazio M, Galletta A, Villari M, Romano A (2020) Blockchain-based healthcare workflow for tele-medical laboratory in federated hospital IoT clouds. Sensors 20(9):2590. https://doi.org/10.3390/s20092590

Chalkiadakis I (2018) A brief survey of visualization methods for deep learning models from the perspective of explainable AI

Chamola V, Goyal A, Sharma P, Hassija V, Binh HTT, Saxena V (2022) Artificial intelligence-assisted blockchain-based framework for smart and secure EMR management. Neural Compt Appl. https://doi.org/10.1007/s00521-022-07087-7

Chandra R, Bansal C, Kang M, Blau T, Agarwal V, Singh P, Wilson LO, Vasan S (2023) Unsupervised machine learning framework for discriminating major variants of concern during COVID-19. PLoS ONE 18(5):e0285719. https://doi.org/10.1371/journal.pone.0285719

Chen S, Zhao Q (2018) Shallowing deep networks: Layer-wise pruning based on feature representations. IEEE Trans Pattern Anal Mach Intell 41(12):3048–3056. https://doi.org/10.1109/TPAMI.2018.2874634

Article   MathSciNet   Google Scholar  

Chen HS, Jarrell JT, Carpenter KA, Cohen DS, Huang X (2019) Blockchain in healthcare: a patient-centered model. Biomed J Sci Tech Res 20(3):15017–15022

Chen X, Ji J, Luo C, Liao W, Li P (2018) When machine learning meets blockchain: a decentralized, privacy-preserving and secure design. 2018 IEEE International Conference on Big Data (Big Data), IEEE. https://doi.org/10.1109/BigData.2018.8622598

Chen C, Wu Y, Dai Q, Zhou H-Y, Xu M, Yang S, Han X, Yu Y (2022) A survey on graph neural networks and graph transformers in computer vision: a task-oriented perspective. https://arxiv.org/abs/2209.13232 . https://doi.org/10.48550/arXiv.2209.13232

Cheng X, Chen F, Xie D, Sun H, Huang C (2020) Design of a secure medical data sharing scheme based on blockchain. J Med Syst 44(2):1–11. https://doi.org/10.1007/s10916-019-1468-1

Cheng AS, Guan Q, Su Y, Zhou P, Zeng Y (2021) Integration of machine learning and blockchain technology in the healthcare field: a literature review and implications for cancer care. Asia Pac J Oncol Nurs 8(6):720–724. https://doi.org/10.4103/apjon.apjon-2140

Choudhary T, Mishra V, Goswami A, Sarangapani J (2020) A comprehensive survey on model compression and acceleration. Artif Intell Rev 53(7):5113–5155. https://doi.org/10.1007/s10462-020-09816-7

Chua LO, Roska T (1993) The CNN paradigm. IEEE Trans Circuits Syst I Fund Theory Appl 40(3):147–156. https://doi.org/10.1109/81.222795

Churi P, Pawar A, Moreno-Guerrero A-J (2021) A comprehensive survey on data utility and privacy: taking Indian healthcare system as a potential case study. Inventions 6(3):45. https://doi.org/10.3390/inventions6030045

Cockrell C, Larie D, An G (2022) Preparing for the next Pandemic: Simulation-based Deep Reinforcement Learning to discover and test multimodal control of systemic inflammation using repurposed immunomodulatory agents. bioRxiv. https://doi.org/10.3389/fimmu.2022.995395

Cui L, Xie X, Shen Z, Lu R, Wang H (2018) Prediction of the healthcare resource utilization using multi-output regression models. IISE Trans Healthc Syst Eng 8(4):291–302. https://doi.org/10.1080/24725579.2018.1512537

Dagher GG, Mohler J, Milojkovic M, Marella PB (2018) Ancile: privacy-preserving framework for access control and interoperability of electronic health records using blockchain technology. Sustain Cities Soc 39:283–297

Dannen C (2017) Introducing ethereum and solidity. Springer. https://doi.org/10.1007/978-1-4842-2535-6

Dan-Sebastian B, Delia-Alexandrina M, Sergiu N, Radu B (2020) Adversarial graph learning and deep learning techniques for improving diagnosis within CT and ultrasound images. 2020 IEEE 16th international conference on intelligent computer communication and processing (ICCP), IEEE. https://doi.org/10.1109/ICCP51029.2020.9266242

Das AK, Bera B, Giri D (2021) Ai and blockchain-based cloud-assisted secure vaccine distribution and tracking in iomt-enabled covid-19 environment. IEEE Internet Things Mag 4(2):26–32. https://doi.org/10.1109/IOTM.0001.2100016

Das AK, Bera B, Giri D (2021) Ai and blockchain-based cloud-assisted secure vaccine distribution and tracking in iomt-enabled covid-19 environment. IEEE Internet Things M 4(2):26–32. https://doi.org/10.1109/IOTM.0001.2100016

Das S, Nayak G, Saba L, Kalra M, Suri JS, Saxena S (2022) An artificial intelligence framework and its bias for brain tumor segmentation: a narrative review. Comput Biol Med. https://doi.org/10.1016/j.compbiomed.2022.105273

Davenport T, Kalakota R (2019) The potential for artificial intelligence in healthcare. Future Healthcare J 6(2):94. https://doi.org/10.7861/futurehosp.6-2-94

Dib O, Brousmiche K-L, Durand A, Thea E, Hamida EB (2018) Consortium blockchains: overview, applications and challenges. Int J Adv Telecommun 11(1):51–64

Dillenberger DN, Novotny P, Zhang Q, Jayachandran P, Gupta H, Hans S, Verma D, Chakraborty S, Thomas J, Walli M (2019) Blockchain analytics and artificial intelligence. IBM J Res Dev 63(2/3):1–5. https://doi.org/10.1147/JRD.2019.2900638

Dilmaghani S, Brust MR, Danoy G, Cassagnes N, Pecero J, Bouvry P (2019) Privacy and security of big data in AI systems: a research and standards perspective. 2019 IEEE International Conference on Big Data (Big Data), IEEE. https://doi.org/10.1109/BigData47090.2019.9006283

Dinh TTA, Wang J, Chen G, Liu R, Ooi BC, Tan K-L (2017) Blockbench: a framework for analyzing private blockchains. Proceedings of the 2017 ACM international conference on management of data. https://doi.org/10.1109/MC.2018.3620971

Dinh TN, Thai MT (2018) Ai and blockchain: a disruptive integration. Computer 51(9):48–53. https://doi.org/10.1109/MC.2018.3620971

Doi K, MacMahon H, Katsuragawa S, Nishikawa RM, Jiang Y (1999) Computer-aided diagnosis in radiology: potential and pitfalls. Eur J Radiol 31(2):97–109. https://doi.org/10.1016/s0720-048x(99)00016-9

Dorri A, Kanhere SS, Jurdak R (2016) Blockchain in internet of things: challenges and solutions. https://arxiv.org/abs/1608.05187 . https://doi.org/10.1016/j.bcra.2021.100006

Duan Y, Edwards JS, Dwivedi YK (2019) Artificial intelligence for decision making in the era of Big Data—evolution, challenges and research agenda. Int J Inf Manage 48:63–71. https://doi.org/10.1016/j.ijinfomgt.2019.01.021

Durga R, Poovammal E (2022) FLED-block: federated learning ensembled deep learning blockchain model for COVID-19 prediction. Front Public Health. https://doi.org/10.3389/fpubh.2022.892499

Ekblaw A, Azaria A, Halamka JD, Lippman A (2016) A case study for blockchain in healthcare:“MedRec” prototype for electronic health records and medical research data. Proc IEEE Open Big Data Conf. https://doi.org/10.1109/OBD.2016.11

El Rifai O, Biotteau M, de Boissezon X, Megdiche I, Ravat F, Teste O (2020) Blockchain-based federated learning in medicine. Int Conf Artif Intell Med. https://doi.org/10.1007/978-3-030-59137-3

Ellahham S (2020) Artificial intelligence: the future for diabetes care. Am J Med 133(8):895–900. https://doi.org/10.1109/JIOT.2023.3263598

Estiri H, Strasser ZH, Rashidian S, Klann JG, Wagholikar KB, McCoy TH, Murphy SN (2022) An objective framework for evaluating unrecognized bias in medical AI models predicting COVID-19 outcomes. J Am Med Inform Assoc. https://doi.org/10.1093/jamia/ocac070

Mihalis K (2020) Ten technologies to fight coronavirus. European Parliamentary Research Service

Fan W, Ma Y, Li Q, He Y, Zhao E, Tang J, Yin D (2019) Graph neural networks for social recommendation. World Wide Web Conf. https://doi.org/10.1109/TKDE.2020.3008732

Fan S, Zhang H, Zeng Y, Cai W (2020b) Hybrid blockchain-based resource trading system for federated learning in edge computing. IEEE Internet Things J 8(4):2252–2264. https://doi.org/10.1109/JIOT.2020.3028101

Fan H, Zhang F, Wang R, Xi L, Li Z (2020) Correlation-aware deep generative model for unsupervised anomaly detection. Advances in Knowledge Discovery and Data Mining: 24th Pacific-Asia Conference, PAKDD 2020, Singapore, May 11–14, 2020, Proceedings, Part II 24, Springer. https://doi.org/10.1109/JIOT.2022.3150048

Farki A, Salekshahrezaee Z, Tofigh AM, Ghanavati R, Arandian B, Chapnevis A (2021) Covid-19 diagnosis using capsule network and fuzzy-means and mayfly optimization algorithm. BioMed Res Int. https://doi.org/10.1155/2021/2295920

Feng Q, He D, Zeadally S, Khan MK, Kumar N (2019) A survey on privacy protection in blockchain system. J Netw Comput Appl 126:45–58. https://doi.org/10.1016/j.jnca.2018.10.020

Feng L, Yang Z, Guo S, Qiu X, Li W, Yu P (2021) Two-layered blockchain architecture for federated learning over mobile edge network. IEEE Netw. https://doi.org/10.1109/MNET.011.2000339

FitzGerald C, Hurst S (2017) Implicit bias in healthcare professionals: a systematic review. BMC Med Ethics 18(1):1–18. https://doi.org/10.1186/s12910-017-0179-8

Fleurence RL, Curtis LH, Califf RM, Platt R, Selby JV, Brown JS (2014) Launching PCORnet, a national patient-centered clinical research network. J Am Med Inform Assoc 21(4):578–582. https://doi.org/10.1136/amiajnl-2014-002747

Frade J, Pereira T, Morgado J, Silva F, Freitas C, Mendes J, Negrão E, De Lima BF (2022) Multiple instance learning for lung pathophysiological findings detection using CT scans. Med Biol Eng Comput 60(6):1569–1584. https://doi.org/10.1007/s11517-022-02526-y

Francisco K, Swanson D (2018) The supply chain has no clothes: technology adoption of blockchain for supply chain transparency. Logistics 2(1):2. https://doi.org/10.3390/logistics2010002

Frank E, Olaoye G (2024) Privacy and data protection in AI-enabled healthcare systems

Fujita S, Han X-H (2020) Cell detection and segmentation in microscopy images with improved mask R-CNN. Proc Asian Conf Comput vis. https://doi.org/10.1007/978-3-030-69756-

Funk E, Riddell J, Ankel F, Cabrera D (2018) Blockchain technology: a data framework to improve validity, trust, and accountability of information exchange in health professions education. Acad Med 93(12):1791–1794. https://doi.org/10.1097/ACM.0000000000002326

Ganggayah MD, Taib NA, Har YC, Lio P, Dhillon SK (2019) Predicting factors for survival of breast cancer patients using machine learning techniques. BMC Med Inform Decis Mak 19(1):1–17. https://doi.org/10.1186/s12911-019-0801-4

Gangwal A, Gangavalli HR, Thirupathi A (2023) A survey of layer-two blockchain protocols. J Netw Comput Appl 209:103539. https://doi.org/10.1016/j.jnca.2022.103539

Gao F, Wu T, Chu X, Yoon H, Xu Y, Patel B (2019) Deep residual inception encoder–decoder network for medical imaging synthesis. IEEE J Biomed Health Inform 24(1):39–49. https://doi.org/10.1109/JBHI.2019.2912659

Giger ML, Suzuki K (2008) Computer-aided diagnosis. Biomedical information technology. Elsevier: 359-XXII. https://doi.org/10.1109/JBHI.2019.2912659

Gordon WJ, Catalini C (2018) Blockchain technology for healthcare: facilitating the transition to patient-driven interoperability. Comput Struct Biotechnol J 16:224–230. https://doi.org/10.1016/j.csbj.2018.06.003

Gori M, Monfardini G, Scarselli F (2005) A new model for learning in graph domains. Proceedings 2005 IEEE International Joint Conference on Neural Networks, IEEE. https://doi.org/10.1109/IJCNN.2005.1555942

Gräther W, Kolvenbach S, Ruland R, Schütte J, Torres C, Wendland F (2018) Blockchain for education: lifelong learning passport. Proceedings of 1st ERCIM Blockchain workshop 2018, European Society for Socially Embedded Technologies (EUSSET). https://doi.org/10.18420/blockchain2018_07

Gropper A (2016) Powering the physician-patient relationship with HIE of one blockchain health IT. ONC/NIST use of Blockchain for healthcare and research workshop. Gaithersburg, Maryland, United States: ONC/NIST. http://bit.ly/BlockchainHealth

Gu W, Gao F, Li R, Zhang J (2021) Learning universal network representation via link prediction by graph convolutional neural network. J Soc Comput 2(1):43–51. https://doi.org/10.23919/JSC.2021.0001

Guo R, Shi H, Zheng D, Jing C, Zhuang C, Wang Z (2019) Flexible and efficient blockchain-based ABE scheme with multi-authority for medical on demand in telemedicine system. IEEE Access 7:88012–88025. https://doi.org/10.1109/ACCESS.2019.2925625

Gupta R, Shukla A, Tanwar S (2020a) BATS: a blockchain and AI-empowered drone-assisted telesurgery system towards 6G. IEEE Trans Netw Sci Eng. https://doi.org/10.1109/TNSE.2020.3043262

Gupta R, Tanwar S, Tyagi S, Kumar N (2020b) Machine learning models for secure data analytics: a taxonomy and threat model. Comput Commun 153:406–440. https://doi.org/10.1016/j.comcom.2020.02.008

Gupta P, Siddiqui MK, Huang X, Morales-Menendez R, Panwar H, Terashima-Marin H, Wajid MS (2022) COVID-WideNet—a capsule network for COVID-19 detection. Appl Soft Comput 122:108780. https://doi.org/10.1016/j.asoc.2022.108780

Gupta R, Tanwar S, Tyagi S, Kumar N, Obaidat MS, Sadoun B (2019) HaBiTs: blockchain-based telesurgery framework for healthcare 4.0. 2019 international conference on computer, information and telecommunication systems (CITS), IEEE. https://doi.org/10.1109/CITS.2019.8862127

Haddad A, Habaebi MH, Islam MR, Hasbullah NF, Zabidi SA (2022) Systematic review on AI-blockchain based E-healthcare records management systems. IEEE Access. https://doi.org/10.1109/ACCESS.2022.3201878

Halevy A, Norvig P, Pereira F (2009) The unreasonable effectiveness of data. IEEE Intell Syst 24(2):8–12. https://doi.org/10.1109/MIS.2009.36

Hamet P, Tremblay J (2017) Artificial intelligence in medicine. Metabolism 69:S36–S40. https://doi.org/10.4103/jfmpc.jfmpc_440_19

Hamid A, Shiekh N, Said N, Ahmad K, Gul A, Hassan L, Al-Fuqaha A (2020) Fake news detection in social media using graph neural networks and NLP Techniques: a COVID-19 use-case. https://arxiv.org/abs/2012.07517 . https://doi.org/10.13140/RG.2.2.26073.34407

Hamze L (2021) Blockchain-based solution for COVID-19 vaccine distribution. Worcester Polytech Inst. https://doi.org/10.1109/EMR.2022.3145656

Han S, Pool J, Tran J, Dally W (2015) Learning both weights and connections for efficient neural network. Advances in neural information processing systems 28. https://doi.org/10.48550/arXiv.1506.02626

Hang L, Kim B, Kim K, Kim D (2021) A permissioned blockchain-based clinical trial service platform to improve trial data transparency. BioMed Res Int. https://doi.org/10.1155/2021/5554487

Hao Z, Wang G, Tian C, Zhang B (2023) A distributed computation model based on federated learning integrates heterogeneous models and consortium blockchain for solving time-varying problems. https://arxiv.org/abs/2306.16023 . https://doi.org/10.48550/arXiv.2306.16023

Hasselgren A, Rensaa J-AH, Kralevska K, Gligoroski D, Faxvaag A (2021) Blockchain for increased trust in virtual health care: proof-of-concept study. J Med Internet Res 23(7):e28496. https://doi.org/10.2196/28496

Hasselgren A, Wan PK, Horn M, Kralevska K, Gligoroski D, Faxvaag A (2020) GDPR compliance for blockchain applications in healthcare. arXiv:2009.12913 . https://doi.org/10.48550/arXiv.2009.12913

He Q, Feng Z, Fang H, Wang X, Zhao L, Yao Y, Yu K (2023) A blockchain-based scheme for secure data offloading in healthcare with deep reinforcement learning. IEEE/ACM Trans Netw. https://doi.org/10.1109/TNET.2023.3274631

Heidari A, Toumaj S, Navimipour NJ, Unal M (2022) A privacy-aware method for COVID-19 detection in chest CT images using lightweight deep conventional neural network and blockchain. Comput Biol Med 145:105461. https://doi.org/10.1016/j.compbiomed.2022.105461

Hepp T, Schoenhals A, Gondek C, Gipp B (2018) OriginStamp: A blockchain-backed system for decentralized trusted timestamping. IT-Information Technology 60(56):273–281. https://doi.org/10.1515/itit-2018-0020

Hernandez I, Zhang Y (2017) Using predictive analytics and big data to optimize pharmaceutical outcomes. Am J Health Syst Pharm 74(18):1494–1500. https://doi.org/10.2146/ajhp161011

Hölbl M, Kompara M, Kamišalić A, Nemec Zlatolas L (2018) A systematic review of the use of blockchain in healthcare. Symmetry 10(10):470. https://doi.org/10.3390/sym10100470

Höppner F, Klawonn F, Kruse R, Runkler T (1999) Fuzzy cluster analysis: methods for classification, data analysis and image recognition. John Wiley & Sons. https://doi.org/10.2307/254022

Hu R, Guo Y, Li H, Pei Q, Gong Y (2020) Personalized federated learning with differential privacy. IEEE Internet Things J 7(10):9530–9539. https://doi.org/10.1109/JIOT.2020.2991416

Hu Q, Yan B, Han Y, Yu J (2021a) An improved delegated proof of stake consensus algorithm. Procedia Comput Sci 187:341–346. https://doi.org/10.1016/j.procs.2021.04.109

Hu Y, Kuang W, Qin Z, Li K, Zhang J, Gao Y, Li W, Li K (2021b) Artificial intelligence security: threats and countermeasures. ACM Comput Surv (CSUR) 55(1):1–36. https://doi.org/10.1145/3487890

Huang Z, Dong W, Duan H, Liu J (2017) A regularized deep learning approach for clinical risk prediction of acute coronary syndrome using electronic health records. IEEE Trans Biomed Eng 65(5):956–968. https://doi.org/10.1109/TBME.2017.2731158

Huang H, Zhu P, Xiao F, Sun X, Huang Q (2020) A blockchain-based scheme for privacy-preserving and secure sharing of medical data. Comput Secur 99:102010. https://doi.org/10.1016/j.cose.2020.102010

Huang J, Qi YW, Asghar MR, Meads A, Tu Y-C (2019) MedBloc: a blockchain-based secure EHR system for sharing and accessing medical data. 2019 18th IEEE international conference on trust, security and privacy in computing and communications/13th IEEE international conference on big data science and engineering (TrustCom/BigDataSE), IEEE. https://doi.org/10.1109/JIOT.2020.2991416

Ienca M, Vayena E (2020) On the responsible use of digital data to tackle the COVID-19 pandemic. Nat Med 26(4):463–464. https://doi.org/10.1038/s41591-020-0832-5

Islam I, Munim KM, Oishwee SJ, Islam AN, Islam MN (2020) A critical review of concepts, benefits, and pitfalls of blockchain technology using concept map. IEEE Access 8:68333–68341. https://doi.org/10.1109/ACCESS.2020.2985647

Islam MR, Rahman MM, Mahmud M, Rahman MA, Mohamad MHS (2021) A review on blockchain security issues and challenges. 2021 IEEE 12th Control and System Graduate Research Colloquium (ICSGRC), IEEE. https://doi.org/10.1109/ICSGRC53186.2021.9515276

Jabarulla MY, Lee H-N (2021a) Blockchain-based distributed patient-centric image management system. Appl Sci 11(1):196. https://doi.org/10.3390/app11010196

Jabarulla MY, Lee H-N (2021b) A blockchain and artificial intelligence-based, patient-centric healthcare system for combating the COVID-19 pandemic: opportunities and applications. Healthc Multidisc Digit Publish Inst. https://doi.org/10.3390/healthcare9081019

Jaeger S, Candemir S, Antani S, Wáng Y-XJ, Lu P-X, Thoma G (2014) Two public chest X-ray datasets for computer-aided screening of pulmonary diseases. Quant Imaging Med Surg 4(6):475. https://doi.org/10.3978/j.issn.2223-4292.2014.11.20

Jahan N, Naveed S, Zeshan M, Tahir MA (2016) How to conduct a systematic review: a narrative literature review. Cureus. https://doi.org/10.7759/cureus.864

Jain PK, Sharma N, Saba L, Paraskevas KI, Kalra MK, Johri A, Nicolaides AN, Suri JS (2021) Automated deep learning-based paradigm for high-risk plaque detection in B-mode common carotid ultrasound scans: an asymptomatic Japanese cohort study. Int Angiol 41:9–23. https://doi.org/10.23736/S0392-9590.21.04771-4

Jain PK, Sharma N, Kalra MK, Johri A, Saba L, Suri JS (2022) Far wall plaque segmentation and area measurement in common and internal carotid artery ultrasound using U-series architectures: an unseen artificial intelligence paradigm for stroke risk assessment. Comput Biol Med 149:106017. https://doi.org/10.1016/j.compbiomed.2022.106017

Jamthikar A, Gupta D, Khanna NN, Saba L, Laird JR, Suri JS (2020) Cardiovascular/stroke risk prevention: a new machine learning framework integrating carotid ultrasound image-based phenotypes and its harmonics with conventional risk factors. Indian Heart J 72(4):258–264. https://doi.org/10.1016/j.ihj.2020.06.004

Jamthikar AD, Gupta D, Mantella LE, Saba L, Johri AM, Suri JS (2021a) Ensemble machine learning and its validation for prediction of coronary artery disease and acute coronary syndrome using focused carotid ultrasound. IEEE Trans Instrum Meas 71:1–10. https://doi.org/10.1109/TIM.2021.3139693

Jamthikar AD, Gupta D, Mantella LE, Saba L, Laird JR, Johri AM, Suri JS (2021b) Multiclass machine learning vs. conventional calculators for stroke/CVD risk assessment using carotid plaque predictors with coronary angiography scores as gold standard: a 500 participants study. Int J Cardiovasc Imaging 37(4):1171–1187. https://doi.org/10.1007/s10554-020-02099-7

Jena B, Saxena S, Nayak GK, Saba L, Sharma N, Suri JS (2021) Artificial intelligence-based hybrid deep learning models for image classification: the first narrative review. Comput Biol Med 137:104803

Jennath H, Anoop V, Asharaf S (2020) Blockchain for healthcare: securing patient data and enabling trusted articial intelligence. Int J Interact Multimedia Artif Intell 6(3):1. https://doi.org/10.9781/ijimai.2020.07.002

Jensen BM, Whyte C, Cuomo S (2020) Algorithms at war: the promise, peril, and limits of artificial intelligence. Int Stud Rev 22(3):526–550. https://doi.org/10.1093/isr/viz025

Ji Q, Sun Y, Gao J, Hu Y, Yin B (2021) A decoder-free variational deep embedding for unsupervised clustering. IEEE Trans Neural Netw Learning Syst 33(10):5681–5693. https://doi.org/10.1109/TNNLS.2021.3071275

Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, Wang Y, Dong Q, Shen H, Wang Y (2017) Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol 2(4):1. https://doi.org/10.1136/svn-2017-000101

Jin H, Dai X, Xiao J, Li B, Li H, Zhang Y (2021) Cross-cluster federated learning and blockchain for internet of medical things. IEEE Internet Things J. https://doi.org/10.1136/svn-2017-000101

Jin S, Liu G, Bai Q (2023) Deep learning in COVID-19 diagnosis, prognosis and treatment selection. Mathematics 11(6):1279. https://doi.org/10.3390/math11061279

Johnson AE, Pollard TJ, Shen L, Lehman L-WH, Feng M, Ghassemi M, Moody B, Szolovits P, Anthony Celi L, Mark RG (2016) MIMIC-III, a freely accessible critical care database. Sci Data 3(1):1–9. https://doi.org/10.1038/sdata.2016.35

Joshi AP, Han M, Wang Y (2018) A survey on security and privacy issues of blockchain technology. Math Found Comput 1(2):121. https://doi.org/10.1109/ICSSE.2019.8823094

Kabir H, Marlow D (2022) It is not always ethical: data manipulation to justify public policy choices in COVID-19 response. smart trends in computing and communications, Springer. pp 239–246. https://doi.org/10.1007/978-981-16-4016-2_23

Kalapaaking AP, Khalil I, Yi X (2023) Blockchain-based federated learning with SMPC model verification against poisoning attack for healthcare systems. IEEE Trans Emerg Top Comput. https://doi.org/10.1109/TETC.2023.3268186

Kalla A, Hewa T, Mishra RA, Ylianttila M, Liyanage M (2020) The role of blockchain to fight against COVID-19. IEEE Eng Manage Rev 48(3):85–96. https://doi.org/10.1109/EMR.2020.3014052

Kamenivskyy Y, Palisetti A, Hamze L, Saberi S (2022) A blockchain-based solution for COVID-19 vaccine distribution. IEEE Eng Manage Rev 50(1):43–53. https://doi.org/10.1109/EMR.2022.3145656

Kan HJ, Kharrazi H, Chang H-Y, Bodycombe D, Lemke K, Weiner JP (2019) Exploring the use of machine learning for risk adjustment: a comparison of standard and penalized linear regression models in predicting health care costs in older adults. PLoS ONE 14(3):e0213258. https://doi.org/10.1371/journal.pone.0213258

Kapadiya K, Patel U, Gupta R, Alshehri MD, Tanwar S, Sharma G, Bokoro PN (2022) Blockchain and AI-empowered healthcare insurance fraud detection: an analysis, architecture, and future prospects. IEEE Access 10:79606–79627. https://doi.org/10.1109/ACCESS.2022.3194569

Kapengut E, Mizrach B (2023) An event study of the ethereum transition to proof-of-stake. Commodities 2(2):96–110. https://doi.org/10.3390/commodities2020006

Katuwal GJ, Pandey S, Hennessey M, Lamichhane B (2018) Applications of blockchain in healthcare: current landscape & challenges. https://doi.org/10.48550/arXiv.1812.02776

Kazancoglu Y, Sezer MD, Ozbiltekin-Pala M, Kucukvar M (2022) Investigating the role of stakeholder engagement for more resilient vaccine supply chains during COVID-19. Operations Management Research. pp 1–12 https://doi.org/10.1007/s12063-021-00223-x

Khan MF, AbaOud M (2023) Blockchain-integrated security for real-time patient monitoring in the internet of medical things using federated learning. IEEE Access. https://doi.org/10.1109/ACCESS.2023.3326155

Khatri S, Alzahrani FA, Ansari MTJ, Agrawal A, Kumar R, Khan RA (2021) A systematic analysis on blockchain integration with healthcare domain: scope and challenges. IEEE Access. https://doi.org/10.1109/ACCESS.2021.3087608

Kim S-K, Huh J-H (2020) Artificial neural network blockchain techniques for healthcare system: focusing on the personal health records. Electronics 9(5):763. https://doi.org/10.3390/electronics9050763

King S, Nadal S (2012) Ppcoin: Peer-to-peer crypto-currency with proof-of-stake. self-published paper, August 19(1)

Kirillov A, He K, Girshick R, Rother C, Dollár P (2019) Panoptic segmentation. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. http://arxiv.org/pdf/1801.00868.pdf

Kohli MD, Summers RM, Geis JR (2017) Medical image data and datasets in the era of machine learning—whitepaper from the 2016 C-MIMI meeting dataset session. J Digit Imaging 30(4):392–399. https://doi.org/10.1007/s10278-017-9976-3

Konstantonis G, Singh KV, Sfikakis PP, Jamthikar AD, Kitas GD, Gupta SK, Saba L, Verrou K, Khanna NN, Ruzsa Z (2022) Cardiovascular disease detection using machine learning and carotid/femoral arterial imaging frameworks in rheumatoid arthritis patients. Rheumatol Int 42(2):215–239. https://doi.org/10.1007/s00296-021-05062-4

Krittanawong C, Rogers AJ, Aydar M, Choi E, Johnson KW, Wang Z, Narayan SM (2020) Integrating blockchain technology with artificial intelligence for cardiovascular medicine. Nat Rev Cardiol 17(1):1–3. https://doi.org/10.1038/s41569-019-0294-y

Kumar SS, Kumar KA (2013) Neural networks in medical and healthcare. Int J Innov Res Dev 2(8):241–244. https://doi.org/10.1371/journal.pone.0212356

Kumar R, Khan AA, Kumar J, Zakria A, Golilarz NA, Zhang S, Ting Y, Zheng C, Wang W (2021a) Blockchain-federated-learning and deep learning models for covid-19 detection using ct imaging. IEEE Sens J. https://doi.org/10.1109/JSEN.2021.3076767

Kumar R, Wang W, Kumar J, Yang T, Khan A, Ali W, Ali I (2021b) An integration of blockchain and AI for secure data sharing and detection of CT images for the hospitals. Comput Med Imaging Graph 87:101812. https://doi.org/10.1016/j.compmedimag.2020.101812

Kumar A, Tripathi AR, Satapathy SC, Zhang Y-D (2022a) SARS-Net: COVID-19 detection from chest x-rays by combining graph convolutional network and convolutional neural network. Pattern Recogn 122:108255. https://doi.org/10.1016/j.patcog.2021.108255

Kumar R, Kumar J, Khan AA, Ali H, Bernard CM, Khan RU, Zeng S (2022b) Blockchain and homomorphic encryption based privacy-preserving model aggregation for medical images. Comput Med Imaging Graph 102:102139. https://doi.org/10.1016/j.compmedimag.2022.10213

Kumar R, Kumar P, Tripathi R, Gupta GP, Islam AN, Shorfuzzaman M (2022c) Permissioned blockchain and deep-learning for secure and efficient data sharing in industrial healthcare systems. IEEE Trans Ind Inf. https://doi.org/10.1109/TII.2022.3161631

Kumar R, Singh D, Srinivasan K, Hu Y-C (2022d) AI-powered blockchain technology for public health: a contemporary review, open challenges, and future research directions. Healthcare. https://doi.org/10.3390/healthcare11010081

Kumar A, Aelgani V, Vohra R, Gupta SK, Bhagawati M, Paul S, Saba L, Suri N, Khanna NN, Laird JR (2024) Artificial intelligence bias in medical system designs: a systematic review. Multimedia Tools Appl 83(6):18005–18057

Kumar N, Parangjothi C, Guru S, Kiran M (2020). peer consonance in blockchain based healthcare application using AI-based consensus mechanism. 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT), IEEE. https://doi.org/10.1109/ICCCNT49239.2020.9225550

Kuo T-T, Kim J, Gabriel RA (2020) Privacy-preserving model learning on a blockchain network-of-networks. J Am Med Inform Assoc 27(3):343–354. https://doi.org/10.1093/jamia/ocz214

Kuppili V, Biswas M, Sreekumar A, Suri HS, Saba L, Edla DR, Marinhoe RT, Sanches JM, Suri JS (2017) Extreme learning machine framework for risk stratification of fatty liver disease using ultrasound tissue characterization. J Med Syst 41(10):1–20. https://doi.org/10.1007/s10916-017-0797-1

Kwon J, Buchman E (2019) Cosmos whitepaper. A Netw. Distrib. Ledgers

Lakhan A, Mohammed MA, Nedoma J, Martinek R, Tiwari P, Vidyarthi A, Alkhayyat A, Wang W (2023) Federated-learning based privacy preservation and fraud-enabled blockchain IoMT system for healthcare. IEEE J Biomed Health Inform 27(2):664–672. https://doi.org/10.1109/JBHI.2022.3165945

Lee C, Luo Z, Ngiam KY, Zhang M, Zheng K, Chen G, Ooi BC, Yip WL (2017) Big healthcare data analytics: Challenges and applications. Handbook of large-scale distributed computing in smart healthcare. pp 11–41. https://doi.org/10.1007/978-3-319-58280-1_2 .

Leeming G, Ainsworth J, Clifton DA (2019) Blockchain in health care: hype, trust, and digital health. Lancet 393(10190):2476–2477. https://doi.org/10.1016/S0140-6736(19)30948-1

Lei Z, Gai K, Yu J, Wang S, Zhu L, Choo KK (2023) Efficiency-enhanced blockchain-based client selection in heterogeneous federated learning. 2023 IEEE international conference on blockchain (blockchain), IEEE. https://doi.org/10.1109/Blockchain60715.2023.00053

Li H, Kadav A, Durdanovic I, Samet H, Graf HP (2016) Pruning filters for efficient convnets. https://arxiv.org/abs/1608.08710 . https://doi.org/10.48550/arXiv.1608.08710

Lian Z, Zeng Q, Wang W, Gadekallu TR, Su C (2022) Blockchain-based two-stage federated learning with non-IID data in IoMT system. IEEE Trans Comput Soc Syst. https://doi.org/10.1109/TCSS.2022.3216802

Lian Z, Wang W, Han Z, Su C (2023) Blockchain-based personalized federated learning for internet of medical things. IEEE Trans Sustain Comput. https://doi.org/10.1109/TSUSC.2023.3279111

Linardatos P, Papastefanopoulos V, Kotsiantis S (2020) Explainable AI: a review of machine learning interpretability methods. Entropy 23(1):18. https://doi.org/10.3390/e23010018

Liu L, Li Z (2022) Permissioned blockchain and deep reinforcement learning enabled security and energy efficient healthcare internet of things. IEEE Access 10:53640–53651. https://doi.org/10.1109/ACCESS.2022.3176444

Liu B, Xiao L, Long J, Tang M, Hosam O (2020a) Secure digital certificate-based data access control scheme in blockchain. IEEE Access 8:91751–91760. https://doi.org/10.1109/ACCESS.2020.2993921

Liu X, Tang Z, Li P, Guo S, Fan X, Zhang J (2020b) A graph learning based approach for identity inference in dapp platform blockchain. IEEE Trans Emerg Top Comput 10(1):438–449. https://doi.org/10.1109/TETC.2020.3027309

Liu Y, Zhao B, Zhao Z, Liu J, Lin X, Wu Q, Susilo W (2024) SS-DID: a secure and scalable Web3 decentralized identity utilizing multi-layer sharding blockchain. IEEE Internet Things J 11:25694–25705

Lo SK, Liu Y, Lu Q, Wang C, Xu X, Paik H-Y, Zhu L (2022) Toward trustworthy AI: blockchain-based architecture design for accountability and fairness of federated learning systems. IEEE Internet Things J 10(4):3276–3284. https://doi.org/10.1109/JIOT.2024.3380068

Lotter W, Diab AR, Haslam B, Kim JG, Grisot G, Wu E, Wu K, Onieva JO, Boyer Y, Boxerman JL (2021) Robust breast cancer detection in mammography and digital breast tomosynthesis using an annotation-efficient deep learning approach. Nat Med 27(2):244–249. https://doi.org/10.1038/s41591-020-01174-9

Lu Y, Huang X, Dai Y, Maharjan S, Zhang Y (2019) Blockchain and federated learning for privacy-preserved data sharing in industrial IoT. IEEE Trans Ind Inf 16(6):4177–4186. https://doi.org/10.1109/TII.2019.2942190

Lu Y, Huang X, Zhang K, Maharjan S, Zhang Y (2020) Low-latency federated learning and blockchain for edge association in digital twin empowered 6G networks. IEEE Trans Ind Inf 17(7):5098–5107. https://doi.org/10.1109/TII.2020.3017668

Lu X, Liu P, Ke Y, Zhang H (2021) Network data security sharing system based on blockchain. Multimedia Tools Appl 80:31887–31906. https://doi.org/10.1007/s11042-021-11183-6

MacLean F (2021) Knowledge graphs and their applications in drug discovery. Expert Opin Drug Discov 16(9):1057–1069. https://doi.org/10.1007/978-1-0716-3449-3_9

Malik H, Anees T, Naeem A, Naqvi RA, Loh W-K (2023) Blockchain-federated and deep-learning-based ensembling of capsule network with incremental extreme learning machines for classification of COVID-19 using CT scans. Bioengineering 10(2):203. https://doi.org/10.3390/bioengineering10020203

Mallikarjuna B, Shrivastava G, Sharma M (2021) Blockchain technology: a DNN token-based approach in healthcare and COVID-19 to generate extracted data. Expert Syst. https://doi.org/10.1111/exsy.12778

Mamoshina P, Ojomoko L, Yanovich Y, Ostrovski A, Botezatu A, Prikhodko P, Izumchenko E, Aliper A, Romantsov K, Zhebrak A (2018) Converging blockchain and next-generation artificial intelligence technologies to decentralize and accelerate biomedical research and healthcare. Oncotarget 9(5):5665. https://doi.org/10.18632/oncotarget.22345

Maniruzzaman M, Rahman M, Al-MehediHasan M, Suri HS, Abedin M, El-Baz A, Suri JS (2018a) Accurate diabetes risk stratification using machine learning: role of missing value and outliers. J Med Syst 42(5):1–17. https://doi.org/10.1007/s10916-018-0940-7

Maniruzzaman M, Suri HS, Kumar N, Abedin MM, Rahman MJ, El-Baz A, Bhoot M, Teji JS, Suri JS (2018b) Risk factors of neonatal mortality and child mortality in Bangladesh. J Global Health 8(1):10. https://doi.org/10.7189/jogh.08.010421

Maniruzzaman M, Jahanur Rahman M, Ahammed B, Abedin MM, Suri HS, Biswas M, El-Baz A, Bangeas P, Tsoulfas G, Suri JS (2019a) Statistical characterization and classification of colon microarray gene expression data using multiple machine learning paradigms. Comput Methods Programs Biomed 176:173–193. https://doi.org/10.1016/j.cmpb.2019.04.008

Maniruzzaman M, Rahman MJ, Ahammed B, Abedin MM, Suri HS, Biswas M, El-Baz A, Bangeas P, Tsoulfas G, Suri JS (2019b) Statistical characterization and classification of colon microarray gene expression data using multiple machine learning paradigms. Comput Methods Programs Biomed 176:173–193. https://doi.org/10.1016/j.cmpb.2019.04.008

Mann SP, Savulescu J, Ravaud P, Benchoufi M (2021) Blockchain, consent and prosent for medical research. J Med Ethics 47(4):244–250. https://doi.org/10.1136/medethics-2020-106443

Manogaran G, Mumtaz S, Mavromoustakis CX, Pallis E, Mastorakis G (2021) Artificial intelligence and blockchain-assisted offloading approach for data availability maximization in edge nodes. IEEE Trans Veh Technol 70(3):2404–2412. https://doi.org/10.1109/TVT.2021.3058689

Mantey EA, Zhou C, Anajemba JH, Okpalaoguchi IM, Chiadika OD-M (2021) Blockchain-secured recommender system for special need patients using deep learning. Front Public Health. https://doi.org/10.3389/fpubh.2021.737269

Mantey EA, Zhou C, Srividhya S, Jain SK, Sundaravadivazhagan B (2022) Integrated blockchain-deep learning approach for analyzing the electronic health records recommender system. Front Public Health 10:905265. https://doi.org/10.3389/fpubh.2022.905265

Marar HW, Marar RW (2020) Hybrid blockchain. Jordanian J Comput Inf Technol (JJCIT). https://doi.org/10.5455/jjcit.71-1589089941

Marwala T, Xing B (2018) Blockchain and artificial intelligence. https://arxiv.org/abs/1802.04451 . https://doi.org/10.48550/arXiv.1802.04451

Mashamba-Thompson TP, Crayton ED (2020) Blockchain and artificial intelligence technology for novel coronavirus disease 2019 self-testing. Multidisc Digit Publ Inst 10:198. https://doi.org/10.3390/diagnostics10040198

Meghla TI, Rahman MM, Biswas AA, Hossain JT, Khatun T (2021) Supply chain management with demand forecasting of covid-19 vaccine using blockchain and machine learning. 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT), IEEE. https://doi.org/10.1109/ICCCNT51525.2021.9580006

Mettler M (2016) Blockchain technology in healthcare: the revolution starts here. 2016 IEEE 18th international conference on e-health networking, applications and services (Healthcom), IEEE. https://doi.org/10.1109/HealthCom.2016.7749510

Mohey Eldin A, Hossny E, Wassif K, Omara FA (2023) Federated blockchain system (FBS) for the healthcare industry. Sci Rep 13(1):2569. https://doi.org/10.1038/s41598-023-29813-4

Mohsin A, Zaidan A, Zaidan B, Mohammed K, Albahri OS, Albahri AS, Alsalem M (2021) PSO–Blockchain-based image steganography: towards a new method to secure updating and sharing COVID-19 data in decentralised hospitals intelligence architecture. Multimedia Tools Appl 80(9):14137–14161. https://doi.org/10.1007/s11042-020-10284-y

Mohurle S, Patil M (2017) A brief study of wannacry threat: Ransomware attack. Int J Adv Res Comput Sci 8(5):1938–1940. https://doi.org/10.26483/ijarcs.v8i5.4021

Molinari F, Meiburger KM, Zeng G, Acharya UR, Liboni W, Nicolaides A, Suri JS (2012) Carotid artery recognition system: a comparison of three automated paradigms for ultrasound images. Med Phys 39(1):378–391. https://doi.org/10.1118/1.3670373

Monday HN, Li J, Nneji GU, Nahar S, Hossin MA, Jackson J (2022) COVID-19 pneumonia classification based on NeuroWavelet capsule network. Healthcare MDPI. https://doi.org/10.3390/healthcare10030422

Monshi MMA, Poon J, Chung V, Monshi FM (2021) CovidXrayNet: optimizing data augmentation and CNN hyperparameters for improved COVID-19 detection from CXR. Comput Biol Med 133:104375. https://doi.org/10.1016/j.compbiomed.2021.104375

Moon TK (1996) The expectation-maximization algorithm. IEEE Signal Process Mag 13(6):47–60. https://doi.org/10.1109/79.543975

Morande S, Marzullo M (2019) Application of artificial intelligence and blockchain in healthcare management-donor organ transplant system. Ann Manag Org Res 1(1):25–38. https://doi.org/10.35912/amor.v1i1.261

Moriya T, Roth HR, Nakamura S, Oda H, Nagara K, Oda M, Mori K (2018) Unsupervised segmentation of 3D medical images based on clustering and deep representation learning. Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging, SPIE. https://doi.org/10.1117/12.2293414

Muhammad G, Hossain MS (2021) A deep-learning-based edge-centric COVID-19-like pandemic screening and diagnosis system within a B5G framework using blockchain. IEEE Network 35(2):74–81. https://doi.org/10.1109/MNET.011.2000326

Mukhometzianov R, Carrillo J (2018) CapsNet comparative performance evaluation for image classification. https://arxiv.org/abs/1805.11195 . https://doi.org/10.48550/arXiv.1805.11195

Munoz D-J, Constantinescu D-A, Asenjo R, Fuentes L (2019) Clinicappchain: a low-cost blockchain hyperledger solution for healthcare. Int Congr Blockchain Appl. https://doi.org/10.1007/978-3-030-23813-1_5

Musamih A, Jayaraman R, Salah K, Hasan HR, Yaqoob I, Al-Hammadi Y (2021) Blockchain-based solution for distribution and delivery of COVID-19 vaccines. IEEE Access 9:71372–71387. https://doi.org/10.1109/EMR.2022.3145656

Myrzashova R, Alsamhi SH, Shvetsov AV, Hawbani A, Wei X (2023) Blockchain meets federated learning in healthcare: a systematic review with challenges and opportunities. IEEE Internet Things J. https://doi.org/10.1109/JIOT.2023.3263598

Nakamoto S (2008) Bitcoin: a peer-to-peer electronic cash system. Decentralized business review: 21260

Narayanan A, Bonneau J, Felten E, Miller A, Goldfeder S (2016) Bitcoin and cryptocurrency technologies: a comprehensive introduction, Princeton University Press. https://lccn.loc.gov/2016014802

Naudé W (2020) Artificial intelligence against COVID-19: an early review. https://doi.org/10.3389/fmed.2021.704256

Nguyen DC, Ding M, Pathirana PN, Seneviratne A (2021a) Blockchain and AI-based solutions to combat coronavirus (COVID-19)-like epidemics: a survey. Ieee Access 9:95730–95753. https://doi.org/10.1109/ACCESS.2021.3093633

Nguyen DC, Ding M, Pham Q-V, Pathirana PN, Le LB, Seneviratne A, Li J, Niyato D, Poor HV (2021b) Federated learning meets blockchain in edge computing: opportunities and challenges. IEEE Internet Things J 8(16):12806–12825. https://doi.org/10.1109/JIOT.2021.3072611

Niranjanamurthy M, Nithya B, Jagannatha S (2019) Analysis of blockchain technology: pros, cons and SWOT. Clust Comput 22(6):14743–14757. https://doi.org/10.1007/s10586-018-2387-5

Nofer M, Gomber P, Hinz O, Schiereck D (2017) Blockchain. Bus Inf Syst Eng 59(3):183–187. https://doi.org/10.1007/s12599-017-0467-3

Noor NM, Than JC, Rijal OM, Kassim RM, Yunus A, Zeki AA, Anzidei M, Saba L, Suri JS (2015) Automatic lung segmentation using control feedback system: morphology and texture paradigm. J Med Syst 39(3):1–18. https://doi.org/10.1007/s10916-015-0214-6

Norori N, Hu Q, Aellen FM, Faraci FD, Tzovara A (2021) Addressing bias in big data and AI for health care: a call for open science. Patterns 2(10):100347. https://doi.org/10.1016/j.patter.2021.100347

Noveck BS (2011) The single point of failure. Innovating Government: Normative, policy and technological dimensions of modern government. pp 77–99. https://doi.org/10.1007/978-90-6704-731-9_6

Oguntola I, Olubeko S, Sweeney C (2018) Slimnets: an exploration of deep model compression and acceleration. 2018 IEEE high performance extreme computing conference (HPEC), IEEE. https://doi.org/10.1109/HPEC.2018.8547604

Omar IA, Jayaraman R, Debe MS, Salah K, Yaqoob I, Omar M (2021) Automating procurement contracts in the healthcare supply chain using blockchain smart contracts. IEEE Access 9:37397–37409. https://doi.org/10.1109/ACCESS.2021.3062471

Onik MM, Aich S, Yang J, Kim CS, Kim HC (2019) Blockchain in healthcare: challenges and solutions. Big data analytics for intelligent healthcare management. Elsevier. pp 197–226. https://doi.org/10.1016/C2018-0-01336-5

Orhan K, Bayrakdar IS, Celik O, Ayan B, Polat E (2021) Can the blockchain-enabled interplanetary file system (Block-IPFS) be a solution for securely transferring imaging data for artificial intelligence research in oral and maxillofacial radiology? Imag Sci Dentistry 51(3):337–339. https://doi.org/10.5624/isd.20210144

Palanivinayagam A, Panneerselvam RK, Kumar P, Rajadurai H, Maheshwari V, Allayear SM (2022) Analysis on COVID-19 infection spread rate during relief schemes using graph theory and deep learning. Computational and Mathematical Methods in Medicine 2022. https://doi.org/10.1155/2022/8131193

Pan J, Song Z, Hao W (2021) Development in consensus protocols: from PoW to PoS to DPoS. 2021 2nd International Conference on Computer Communication and Network Security (CCNS), IEEE. https://doi.org/10.1109/CCNS53852.2021.00020

Panch T, Mattie H, Atun R (2019) Artificial intelligence and algorithmic bias: implications for health systems. J Global Health. https://doi.org/10.7189/jogh.09.020318

Panda NR (2022) A review on logistic regression in medical research. Natl J Commun Med 13(04):265–270. https://doi.org/10.55489/njcm.134202222

Pandl KD, Thiebes S, Schmidt-Kraepelin M, Sunyaev A (2020) On the convergence of artificial intelligence and distributed ledger technology: a scoping review and future research agenda. IEEE Access 8:57075–57095. https://doi.org/10.1109/ACCESS.2020.2981447

Paoletti ME, Moreno-Alvarez S, Haut JM (2021) Multiple attention-guided capsule networks for hyperspectral image classification. IEEE Trans Geosci Remote Sens 60:1–20. https://doi.org/10.1109/TGRS.2021.3135506

Papin G, Bailly S, Dupuis C, Ruckly S, Gainnier M, Argaud L, Azoulay E, Adrie C, Souweine B, Goldgran-Toledano D (2021) Clinical and biological clusters of sepsis patients using hierarchical clustering. PLoS ONE 16(8):e0252793. https://doi.org/10.1371/journal.pone.0252793

Paul S, Maindarkar M, Saxena S, Saba L, Turk M, Kalra M, Krishnan PR, Suri JS (2022) Bias investigation in artificial intelligence systems for early detection of parkinson’s disease: a narrative review. Diagnostics 12(1):166. https://doi.org/10.3390/diagnostics12010166

Peng Y, Wang G, Kou G, Shi Y (2011) An empirical study of classification algorithm evaluation for financial risk prediction. Appl Soft Comput 11(2):2906–2915. https://doi.org/10.1016/j.asoc.2010.11.028

Pereira T, Morgado J, Silva F, Pelter MM, Dias VR, Barros R, Freitas C, Negrão E, Flor de Lima B, Correia da Silva M (2021) Sharing biomedical data: strengthening ai development in healthcare. Healthcare. https://doi.org/10.3390/healthcare9070827

Pergher I, Brandolf VP (2016) A patient-centric approach to improve health care services. Cogent Bus Manag 3(1):1227232. https://doi.org/10.1080/23311975.2016.1227232

Piciarelli C, Mishra P, Foresti GL (2019) Image anomaly detection with capsule networks and imbalanced datasets. Image Analysis and Processing–ICIAP 2019: 20th International Conference, Trento, Italy, September 9–13, 2019, Proceedings, Part I 20, Springer. https://doi.org/10.1142/S0218001421520108

Pierro GA, Tonelli R (2022) Can solana be the solution to the blockchain scalability problem? 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER), IEEE. https://doi.org/10.1109/SANER53432.2022.00144

Pilozzi A, Huang X (2020) Overcoming Alzheimer’s disease stigma by leveraging artificial intelligence and blockchain technologies. Brain Sci 10(3):183. https://doi.org/10.3390/brainsci10030183

Pirruccello JP, Chaffin MD, Chou EL, Fleming SJ, Lin H, Nekoui M, Khurshid S, Friedman SF, Bick AG, Arduini A (2021) Deep learning enables genetic analysis of the human thoracic aorta. Nat Genetics. https://doi.org/10.1038/s41588-021-00962-4

Połap D, Srivastava G, Jolfaei A, Parizi RM (2020) Blockchain technology and neural networks for the internet of medical things. IEEE INFOCOM 2020-IEEE conference on computer communications workshops (INFOCOM WKSHPS), IEEE. https://doi.org/10.1109/INFOCOMWKSHPS50562.2020.9162735

Politou E, Casino F, Alepis E, Patsakis C (2019) Blockchain mutability: challenges and proposed solutions. IEEE Trans Emerg Top Comput. https://doi.org/10.1109/TETC.2019.2949510

Qammar A, Karim A, Ning H, Ding J (2023) Securing federated learning with blockchain: a systematic literature review. Artif Intell Rev 56(5):3951–3985. https://doi.org/10.1007/s10462-022-10271-9

Qu Y, Gao L, Luan TH, Xiang Y, Yu S, Li B, Zheng G (2020) Decentralized privacy using blockchain-enabled federated learning in fog computing. IEEE Internet Things J 7(6):5171–5183. https://doi.org/10.1109/TETC.2019.2949510

Ragab M, Alshehri S, Alhakamy NA, Mansour RF, Koundal D (2022) Multiclass classification of chest X-ray images for the prediction of COVID-19 using capsule network. Comput Intell Neurosci 20:22. https://doi.org/10.1155/2022/6185013

Ragnedda M, Destefanis G (2019) Blockchain and web 3.0. Routledge, London. https://doi.org/10.4324/9780429029530

Book   Google Scholar  

Rahman MA, Hossain MS, Islam MS, Alrajeh NA, Muhammad G (2020) Secure and provenance enhanced Internet of health things framework: a blockchain managed federated learning approach. IEEE Access 8:205071–205087. https://doi.org/10.1109/ACCESS.2020.3037474

Rahman A, Islam MJ, Karim MR, Kundu D, Kabir S (2021) An intelligent vaccine distribution process in COVID-19 pandemic through blockchain-sdn framework from bangladesh perspective. 2021 International Conference on Electronics, Communications and Information Technology (ICECIT), IEEE. https://doi.org/10.1109/ICECIT54077.2021.9641303

Raikwar M, Gligoroski D, Velinov G (2020) Trends in development of databases and blockchain. 2020 seventh international conference on software defined systems (SDS), IEEE. https://doi.org/10.1109/SDS49854.2020.9143893

Rajendra Acharya U, Faust O, Vinitha Sree S, Alvin APC, Krishnamurthi G, Seabra JCR, Sanches J, Suri JS (2013c) Understanding symptomatology of atherosclerotic plaque by image-based tissue characterization. Comput Methods Programs Biomed 110(1):66–75. https://doi.org/10.1016/j.compbiomed.2013.05.024

Rasheed A, Mahapatra RN, Varol C, Narashimha K (2021) Exploiting zero knowledge proof and blockchains towards the enforcement of anonymity, data integrity and privacy (ADIP) in the IoT. IEEE Trans Emerg Top Comput 10(3):1476–1491. https://doi.org/10.1109/TETC.2021.3099701

Reegu FA, Abas H, Gulzar Y, Xin Q, Alwan AA, Jabbari A, Sonkamble RG, Dziyauddin RA (2023) Blockchain-based framework for interoperable electronic health records for an improved healthcare system. Sustainability 15(8):6337. https://doi.org/10.3390/su15086337

Reyna A, Martín C, Chen J, Soler E, Díaz M (2018) On blockchain and its integration with IoT. Challenges and opportunities. Future Gener Comput Syst 88:173–190. https://doi.org/10.1016/j.future.2018.05.046

Saba L, Sanfilippo R, Sannia S, Anzidei M, Montisci R, Mallarini G, Suri JS (2012) Association between carotid artery plaque volume, composition, and ulceration: a retrospective assessment with MDCT. Am J Roentgenol 199(1):151–156. https://doi.org/10.2214/AJR.11.6955

Saba L, Biswas M, Kuppili V, Godia EC, Suri HS, Edla DR, Omerzu T, Laird JR, Khanna NN, Mavrogeni S (2019) The present and future of deep learning in radiology. Eur J Radiol 114:14–24. https://doi.org/10.1016/j.ejrad.2019.02.038

Saba L, Sanagala SS, Gupta SK, Koppula VK, Johri AM, Sharma AM, Kolluri R, Bhatt DL, Nicolaides A, Suri JS (2021) Ultrasound-based internal carotid artery plaque characterization using deep learning paradigm on a supercomputer: a cardiovascular disease/stroke risk assessment system. Int J Cardiovasc Imaging. https://doi.org/10.1007/s10554-020-02124-9

Saba L, Agarwal M, Patrick A, Puvvula A, Gupta SK, Carriero A, Laird JR, Kitas GD, Johri AM, Balestrieri A (2021a) Six artificial intelligence paradigms for tissue characterisation and classification of non-COVID-19 pneumonia against COVID-19 pneumonia in computed tomography lungs. Int J Comput Assist Radiol Surg 16(3):423–434. https://doi.org/10.1007/s11548-021-02317-0

Saba L, Sanagala SS, Gupta SK, Koppula VK, Johri AM, Khanna NN, Mavrogeni S, Laird JR, Pareek G, Miner M (2021b) Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application. Ann Transl Med 9(14):1. https://doi.org/10.21037/atm-20-7676

Saba L, Sanagala SS, Gupta SK, Koppula VK, Laird JR, Viswanathan V, Sanches JM, Kitas GD, Johri AM, Sharma N (2021c) A multicenter study on carotid ultrasound plaque tissue characterization and classification using six deep artificial intelligence models: a stroke application. IEEE Trans Instrum Meas. https://doi.org/10.1109/TIM.2021.3052577

Saba L, Nardi V, Cau R, Gupta A, Kamel H, Suri JS, Balestrieri A, Congiu T, Butler AP, Gieseg S (2022) Carotid artery plaque calcifications: lessons from histopathology to diagnostic imaging. Stroke 53(1):290–297. https://doi.org/10.1161/STROKEAHA.121.035692

Sabour S, Frosst N, Hinton GE (2017) Dynamic routing between capsules. Advances in neural information processing systems 30. https://doi.org/10.48550/arXiv.1710.09829

Sai S, Hassija V, Chamola V, Guizani M (2023) Federated learning and NFT-based privacy-preserving medical data sharing scheme for intelligent diagnosis in smart healthcare. IEEE Internet Things J. https://doi.org/10.1109/JIOT.2023.3308991

Samuel O, Omojo A, Onuja A, Sunday Y, Tiwari P, Gupta D, Hafeez G, Yahaya A, Fatoba O, Shamshirband S (2022) IoMT: a COVID-19 healthcare system driven by federated learning and blockchain. IEEE J Biomed Health Inform. https://doi.org/10.1109/JBHI.2022.3143576

Sanagala SS, Nicolaides A, Gupta SK, Koppula VK, Saba L, Agarwal S, Johri AM, Kalra MS, Suri JS (2021) Ten fast transfer learning models for carotid ultrasound plaque tissue characterization in augmentation framework embedded with heatmaps for stroke risk stratification. Diagnostics 11(11):2109. https://doi.org/10.3390/diagnostics11112109

Sandu N, Karim S (2020) The application of fast CapsNet computer vision in detecting Covid-19. Int J Recent Eng Res Dev 5(6):29–34

Saqib M (2021) Forecasting COVID-19 outbreak progression using hybrid polynomial-Bayesian ridge regression model. Appl Intell 51(5):2703–2713. https://doi.org/10.1007/s10489-020-01942-7

Scheikl PM, Laschewski S, Kisilenko A, Davitashvili T, Müller B, Capek M, Müller-Stich BP, Wagner M, Mathis-Ullrich F (2020) Deep learning for semantic segmentation of organs and tissues in laparoscopic surgery. Current Direct Biomed Eng. https://doi.org/10.1515/cdbme-2020-0016

Schelb P, Kohl S, Radtke JP, Wiesenfarth M, Kickingereder P, Bickelhaupt S, Kuder TA, Stenzinger A, Hohenfellner M, Schlemmer H-P (2019) Classification of cancer at prostate MRI: deep learning versus clinical PI-RADS assessment. Radiology 293(3):607–617. https://doi.org/10.1515/cdbme-2020-0016

Shah SA, Koltun V (2018) Deep continuous clustering. https://arxiv.org/abs/1803.01449 . https://doi.org/10.48550/arXiv.1803.01449

Shang Y, Li S (2022) Hybrid combinatorial remanufacturing strategy for medical equipment in the pandemic. Comput Ind Eng 174:108811. https://doi.org/10.1016/j.cie.2022.108811

Sharma N, Saba L, Khanna NN, Kalra MK, Fouda MM, Suri JS (2022) Segmentation-based classification deep learning model embedded with explainable AI for COVID-19 detection in chest X-ray scans. Diagnostics 12(9):2132. https://doi.org/10.3390/diagnostics12092132

Sharma P, Arya R, Verma R, Verma B (2023) Conv-CapsNet: capsule based network for COVID-19 detection through X-ray scans. Multimedia Tools Appl. https://doi.org/10.1007/s11042-023-14353-w

Shayan M, Fung C, Yoon CJ, Beschastnikh I (2020) Biscotti: a blockchain system for private and secure federated learning. IEEE Trans Parallel Distrib Syst 32(7):1513–1525. https://doi.org/10.1109/TPDS.2020.3044223

Shen M, Wang H, Zhang B, Zhu L, Xu K, Li Q, Du X (2020) Exploiting unintended property leakage in blockchain-assisted federated learning for intelligent edge computing. IEEE Internet Things J 8(4):2265–2275. https://doi.org/10.1109/JIOT.2020.3028110

Shinde R, Patil S, Kotecha K, Potdar V, Selvachandran G, Abraham A (2022) Securing AI-based healthcare systems using blockchain technology: a state-of-the-art systematic literature review and future research directions. https://arxiv.org/abs/2206.04793 . https://doi.org/10.48550/arXiv.2206.04793

Shouman M, Turner T, Stocker R (2012) Applying k-nearest neighbour in diagnosing heart disease patients. Int J Inf Educ Technol 2(3):220–223. https://doi.org/10.7763/IJIET.2012.V2.114

Shrivastava VK, Londhe ND, Sonawane RS, Suri JS (2017) A novel and robust Bayesian approach for segmentation of psoriasis lesions and its risk stratification. Comput Methods Programs Biomed 150:9–22. https://doi.org/10.1016/j.cmpb.2017.07.011

Shuaib M, Alam S, Alam MS, Nasir MS (2021a) Compliance with HIPAA and GDPR in blockchain-based electronic health record. Mater Today. https://doi.org/10.1016/j.matpr.2021.03.059

Shuaib M, Alam S, Alam MS, Nasir MS (2021b) Self-sovereign identity for healthcare using blockchain. Mater Today. https://doi.org/10.1109/ACCESS.2020.2994090

Silitonga P (2017) Clustering of patient disease data by using K-means clustering. Int J Comput Sci Inf Secur (IJCSIS) 15(7):219–221. https://doi.org/10.1109/ICHI.2017.18

Singh S, Sharma PK, Yoon B, Shojafar M, Cho GH, Ra I-H (2020) Convergence of blockchain and artificial intelligence in IoT network for the sustainable smart city. Sustain Cities Soc 63:102364. https://doi.org/10.1016/j.scs.2020.102364

Singh R, Mir BA, Chakravarthi DS, Alharbi AR, Kumar H, Hingaa SK (2022a) Smart healthcare system with light-weighted blockchain system and deep learning techniques. Comput Intell Neurosci. https://doi.org/10.1155/2022/1621258

Singh S, Rathore S, Alfarraj O, Tolba A, Yoon B (2022b) A framework for privacy-preservation of IoT healthcare data using federated learning and blockchain technology. Future Gener Comput Syst 129:380–388. https://doi.org/10.1016/j.future.2021.11.028

Skandha SS, Gupta SK, Saba L, Koppula VK, Johri AM, Khanna NN, Mavrogeni S, Laird JR, Pareek G, Miner M (2020) 3-D optimized classification and characterization artificial intelligence paradigm for cardiovascular/stroke risk stratification using carotid ultrasound-based delineated plaque: atheromatic™ 20. Comput Biol Med 125:103958. https://doi.org/10.1016/j.compbiomed.2020.103958

Skandha SS, Agarwal M, Utkarsh K, Gupta SK, Koppula VK, Suri JS (2022a) A novel genetic algorithm-based approach for compression and acceleration of deep learning convolution neural network: an application in computer tomography lung cancer data. Neural Comput Appl 34(23):20915–20937. https://doi.org/10.1007/s00521-022-07567-w

Skandha SS, Nicolaides A, Gupta SK, Koppula VK, Saba L, Johri AM, Kalra MS, Suri JS (2022b) A hybrid deep learning paradigm for carotid plaque tissue characterization and its validation in multicenter cohorts using a supercomputer framework. Comput Biol Med 141:105131. https://doi.org/10.1016/j.compbiomed.2021.105131

Sonkamble RG, Bongale AM, Phansalkar S, Sharma A, Rajput S (2023) Secure data transmission of electronic health records using blockchain technology. Electronics 12(4):1015. https://doi.org/10.3390/electronics12041015

Sousa MJ, Rocha Á (2019) Digital learning: developing skills for digital transformation of organizations. Future Gener Comput Syst 91:327–334. https://doi.org/10.1016/j.future.2018.08.048

Srinivas S, Babu RV (2015) Data-free parameter pruning for deep neural networks. https://arxiv.org/abs/1507.06149 . https://doi.org/10.1016/j.compbiomed.2021.105131

Srivastava SK, Singh SK, Suri JS (2019) Effect of incremental feature enrichment on healthcare text classification system: a machine learning paradigm. Comput Methods Programs Biomed 172:35–51. https://doi.org/10.1016/j.cmpb.2019.01.011

Stephanie V, Khalil I, Atiquzzaman M, Yi X (2023) Trustworthy privacy-preserving hierarchical ensemble and federated learning in healthcare 4.0 with blockchain. https://doi.org/10.1109/tii.2022.3214998

Strauss JS, Bartko JJ, Carpenter WT (1973) The use of clustering techniques for the classification of psychiatric patients. Br J Psychiatry 122(570):531–540. https://doi.org/10.1017/s0007125000110396

Straw I (2020) The automation of bias in medical artificial intelligence (AI): decoding the past to create a better future. Artif Intell Med 110:101965. https://doi.org/10.1016/j.artmed.2020.101965

Sun X, Yu FR, Zhang P, Sun Z, Xie W, Peng X (2021) A survey on zero-knowledge proof in blockchain. IEEE Network 35(4):198–205. https://doi.org/10.1201/9781003190127-8

Sunny J, Undralla N, Pillai VM (2020) Supply chain transparency through blockchain-based traceability: an overview with demonstration. Comput Ind Eng 150:106895. https://doi.org/10.1016/j.cie.2020.106895

Suo Q, Ma F, Yuan Y, Huai M, Zhong W, Gao J, Zhang A (2018) Deep patient similarity learning for personalized healthcare. IEEE Trans Nanobiosci 17(3):219–227. https://doi.org/10.1109/tnb.2018.2837622

Suri JS, Rangayyan RM (2006) Breast imaging mammography, and computer-aided diagnosis of breast cancer. SPIE. https://doi.org/10.1117/3.651880.ch22

Suri J, Agarwal S, Gupta SK, Puvvula A, Viskovic K, Suri N, Alizad A, El-Baz A, Saba L, Fatemi M (2021a) Systematic review of artificial intelligence in acute respiratory distress syndrome for COVID-19 lung patients: a biomedical imaging perspective. IEEE J Biomed Health Inform. https://doi.org/10.1109/jbhi.2021.3103839

Suri JS, Agarwal S, Pathak R, Ketireddy V, Columbu M, Saba L, Gupta SK, Faa G, Singh IM, Turk M (2021b) COVLIAS 1.0: lung segmentation in COVID-19 computed tomography scans using hybrid deep learning artificial intelligence models. Diagnostics 11(8):1405. https://doi.org/10.1016/b978-0-443-18450-5.00011-6

Suri JS, Agarwal S, Chabert GL, Carriero A, Paschè A, Danna PS, Saba L, Mehmedović A, Faa G, Singh IM (2022a) COVLIAS 2.0-cXAI: cloud-based explainable deep learning system for COVID-19 lesion localization in computed tomography scans. Diagnostics 12(6):1482. https://doi.org/10.21203/rs.3.rs-3688115/v1

Suri JS, Agarwal S, Jena B, Saxena S, El-Baz A, Agarwal V, Kalra MK, Saba L, Viskovic K, Fatemi M (2022b) Five strategies for bias estimation in artificial intelligence-based hybrid deep learning for acute respiratory distress syndrome COVID-19 lung infected patients using AP (ai) bias 2.0: a systematic review. IEEE Trans Instrum Meas. https://doi.org/10.1109/tim.2022.3174270

Suri JS (2008) Advances in diagnostic and therapeutic ultrasound imaging. Artech House

Tagde P, Tagde S, Bhattacharya T, Tagde P, Chopra H, Akter R, Kaushik D, Rahman M (2021) Blockchain and artificial intelligence technology in e-Health. Environ Sci Pollut Res 28(38):52810–52831. https://doi.org/10.37965/jait.2021.0019

Tagliafico AS, Campi C, Bianca B, Bortolotto C, Buccicardi D, Francesca C, Prost R, Rengo M, Faggioni L (2022) Blockchain in radiology research and clinical practice: current trends and future directions. Radiol Med (torino). https://doi.org/10.1007/s11547-022-01460-1

Tan T-E, Anees A, Chen C, Li S, Xu X, Li Z, Xiao Z, Yang Y, Lei X, Ang M (2021) Retinal photograph-based deep learning algorithms for myopia and a blockchain platform to facilitate artificial intelligence medical research: a retrospective multicohort study. Lancet Digital Health 3(5):e317–e329. https://doi.org/10.1016/S2589-7500(21)00055-8

Tandel GS, Balestrieri A, Jujaray T, Khanna NN, Saba L, Suri JS (2020) Multiclass magnetic resonance imaging brain tumor classification using artificial intelligence paradigm. Comput Biol Med 122:103804. https://doi.org/10.1016/j.compbiomed.2020.103804

Tandon A, Albeshri A, Thayananthan V, Alhalabi W, Radicchi F, Fortunato S (2021) Community detection in networks using graph embeddings. Phys Rev E 103(2):022316. https://doi.org/10.1103/PhysRevE.103.022316

Tanwar S, Gupta R, Patel MM, Shukla A, Sharma G, Davidson IE (2021) Blockchain and AI-empowered social distancing scheme to combat COVID-19 situations. IEEE Access 9:129830–129840. https://doi.org/10.1109/ACCESS.2021.3114098

Teji JS, Jain S, Gupta SK, Suri JS (2022) NeoAI 1.0: machine learning-based paradigm for prediction of neonatal and infant risk of death. Comput Biol Medic. https://doi.org/10.1016/j.compbiomed.2022.105639

Tian G, Hu Y, Wei J, Liu Z, Huang X, Chen X, Susilo W (2021) Blockchain-based secure deduplication and shared auditing in decentralized storage. IEEE Trans Dependable Secure Comput. https://doi.org/10.1109/TDSC.2021.3114160

Till BM, Peters AW, Afshar S, Meara JG (2017) From blockchain technology to global health equity: can cryptocurrencies finance universal health coverage? BMJ Glob Health 2(4):e000570

Tith D, Lee J-S, Suzuki H, Wijesundara W, Taira N, Obi T, Ohyama N (2020) Application of blockchain to maintaining patient records in electronic health record for enhanced privacy, scalability, and availability. Healthcare Inf Res 26(1):3–12. https://doi.org/10.4258/hir.2020.26.1.3

Tiwari S, Jain A (2021) Convolutional capsule network for COVID-19 detection using radiography images. Int J Imaging Syst Technol 31(2):525–539. https://doi.org/10.1002/ima.22566

Tjoa E, Guan C (2020) A survey on explainable artificial intelligence (xai): toward medical xai. IEEE Trans Neural Netw Learning Syst 32(11):4793–4813. https://doi.org/10.1109/TNNLS.2020.3027314

Toraman S, Alakus TB, Turkoglu I (2020) Convolutional capsnet: a novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks. Chaos, Solitons Fractals 140:110122. https://doi.org/10.1016/j.chaos.2020.110122

Treleaven P, Brown RG, Yang D (2017) Blockchain technology in finance. Computer 50(9):14–17. https://doi.org/10.1109/MC.2017.3571047

Tripathi S, Tabari A, Mansur A, Dabbara H, Bridge CP, Daye D (2024) From machine learning to patient outcomes: a comprehensive review of AI in pancreatic cancer. Diagnostics 14(2):174. https://doi.org/10.3390/diagnostics14020174

van Egmond MB, Spini G, van der Galien O (2021) Privacy-preserving dataset combination and Lasso regression for healthcare predictions. BMC Med Inform Decis Mak 21(1):1–16. https://doi.org/10.1186/s12911-021-01582-y

Velde F (2013) Bitcoin: a primer

Vyas S, Gupta M, Yadav R (2019) Converging blockchain and machine learning for healthcare. 2019 Amity International Conference on Artificial Intelligence (AICAI), IEEE. https://doi.org/10.1109/AICAI.2019.8701230

Wang K, Dong J, Wang Y, Yin H (2019a) Securing data with blockchain and AI. IEEE Access 7:77981–77989. https://doi.org/10.1109/ACCESS.2019.2921555

Wang X, Mao K, Wang L, Yang P, Lu D, He P (2019b) An appraisal of lung nodules automatic classification algorithms for CT images. Sensors 19(1):194. https://doi.org/10.3390/s19010194

Wang Z, Luo N, Zhou P (2020) GuardHealth: blockchain empowered secure data management and graph convolutional network enabled anomaly detection in smart healthcare. J Parallel Distrib Comput 142:1–12. https://doi.org/10.1016/j.jpdc.2020.03.004

Wang S, Hu L, Wang Y, He X, Sheng QZ, Orgun MA, Cao L, Ricci F, Yu PS (2021) Graph learning based recommender systems: a review. https://arxiv.org/abs/2105.06339 . https://doi.org/10.48550/arXiv.2105

Wang Z, Cai L, Zhang X, Choi C, Su X (2022) A COVID-19 auxiliary diagnosis based on federated learning and blockchain. Computational and Mathematical Methods in Medicine 2022. https://doi.org/10.1155/2022/7078764

Warnat-Herresthal S, Schultze H, Shastry KL, Manamohan S, Mukherjee S, Garg V, Sarveswara R, Händler K, Pickkers P, Aziz NA (2021) Swarm learning for decentralized and confidential clinical machine learning. Nature 594(7862):265–270. https://doi.org/10.1038/s41586-021-03583-3

Webb GI, Keogh E, Miikkulainen R (2010) Naïve bayes. Encyclopedia Mach Learn 15(1):713–714. https://doi.org/10.1007/978-1-4899-7502-7_581-1

Weeks RV (2013) Electronic health records: managing the transformation from a paper-based to and electronic system. J Contemp Manag 10(1):135–155

Werth J, Berenjestanaki MH, Barzegar HR, El Ioini N, Pahl C (2023) A review of blockchain platforms based on the scalability, security and decentralization trilemma. ICEIS 1:146–155. https://doi.org/10.5220/0011837200003467

WHO CO (2020) “World health organization.” Responding to Community Spread of COVID-19. Reference WHO/COVID-19/Community_Transmission/2020.1

Wood G (2016) Polkadot: Vision for a heterogeneous multi-chain framework. White Paper 21:2327–4662

Wright SA (2019) Technical and legal challenges for healthcare blockchains and smart contracts. 2019 ITU Kaleidoscope: ICT for Health: Networks, Standards and Innovation (ITU K), IEEE. https://doi.org/10.23919/ITUK48006.2019.8996146

Wu M, Zhang W, Guo Z, Song J, Zeng Y, Huang Y, Yang Y, Zhang P, Liu J (2021) Separation of normal and impaired dynamic cerebral autoregulation using deep embedded clustering: a proof-of-concept study. Physiol Meas 42(7):074002. https://doi.org/10.1088/1361-6579/ac0e81

Wu B, Pi Y, Chen J (2022) Privacy protection of medical service data based on blockchain and artificial intelligence in the era of smart medical care. Wirel Commun Mobile Comput. https://doi.org/10.1155/2022/5295801

Wu S, Jiao L, Wu Q (2020) ACOL-GAN: learning clustering generative adversarial networks through graph-based activity regularization. Proceedings of the 2020 6th International Conference on Computing and Artificial Intelligence. https://doi.org/10.1145/3404555.3404581

Xia F, Sun K, Yu S, Aziz A, Wan L, Pan S, Liu H (2021) Graph learning: a survey. IEEE Trans Artif Intell 2(2):109–127. https://doi.org/10.48550/arXiv.1904.07275

Xiao Y, Zhang N, Li J, Lou W, Hou YT (2020) PrivacyGuard: enforcing private data usage control with blockchain and attested off-chain contract execution. European symposium on research in computer security, Springer. https://doi.org/10.48550/arXiv.1904.07275

Xie J, Girshick R, Farhadi A (2016) Unsupervised deep embedding for clustering analysis. Int Conf Mach Learn PMLR 48(478–487):2016

Xiong Z, Zhang Y, Niyato D, Wang P, Han Z (2018) When mobile blockchain meets edge computing. IEEE Commun Mag 56(8):33–39. https://doi.org/10.48550/arXiv.1711.05938

Xu T-B, Yang P, Zhang X-Y, Liu C-L (2019) LightweightNet: Toward fast and lightweight convolutional neural networks via architecture distillation. Pattern Recogn 88:272–284. https://doi.org/10.1016/j.patcog.2018.10.029

Yampolskiy RV, Spellchecker M (2016) Artificial intelligence safety and cybersecurity: a timeline of AI failures. https://arxiv.org/abs/1610.07997 . https://doi.org/10.48550/arXiv.1610.07997

Yang X, Li W (2020) A zero-knowledge-proof-based digital identity management scheme in blockchain. Comput Secur 99:102050. https://doi.org/10.1016/j.cose.2020.102050

Yang Q, Liu Y, Cheng Y, Kang Y, Chen T, Yu H (2019) Federated learning. Synth Lectures Artif Intell Mach Learn 13(3):1–207. https://doi.org/10.1007/978-3-031-01585-4_5

Yang L, Li M, Si P, Yang R, Sun E, Zhang Y (2020) Energy-efficient resource allocation for blockchain-enabled industrial internet of things with deep reinforcement learning. IEEE Internet Things J 8(4):2318–2329. https://doi.org/10.1109/JIOT.2020.3030646

Ying X, Liu C, Hu D (2023) GCFL: blockchain-based efficient federated learning for heterogeneous devices. 2023 IEEE Symposium on Computers and Communications (ISCC), IEEE. https://doi.org/10.1109/ISCC58397.2023.10218066

Yu K-H, Beam AL, Kohane IS (2018) Artificial intelligence in healthcare. Nat Biomed Eng 2(10):719–731. https://doi.org/10.1038/s41551-018-0305-z

Yu F, Zhang W, Qin Z, Xu Z, Wang D, Liu C, Tian Z, Chen X (2020) Heterogeneous federated learning. https://doi.org/10.48550/arXiv.2008.06767

Zerka F, Urovi V, Vaidyanathan A, Barakat S, Leijenaar RT, Walsh S, Gabrani-Juma H, Miraglio B, Woodruff HC, Dumontier M (2020) Blockchain for privacy preserving and trustworthy distributed machine learning in multicentric medical imaging (C-DistriM). IEEE Access 8:183939–183951. https://doi.org/10.1109/ACCESS.2020.3029445

Zhang J, Wu Y (2018) Complex-valued unsupervised convolutional neural networks for sleep stage classification. Comput Methods Programs Biomed 164:181–191. https://doi.org/10.1016/j.cmpb.2018.07.015

Zhang P, White J, Schmidt DC, Lenz G, Rosenbloom ST (2018) FHIRChain: applying blockchain to securely and scalably share clinical data. Comput Struct Biotechnol J 16:267–278. https://doi.org/10.1016/j.csbj.2018.07.004

Zhang Y, Xiong F, Xie Y, Fan X, Gu H (2020) The impact of artificial intelligence and blockchain on the accounting profession. IEEE Access 8:110461–110477. https://doi.org/10.1109/ACCESS.2020.3000505

Zhang R, Xue R, Liu L (2021a) Security and privacy for healthcare blockchains. IEEE Trans Serv Comput. https://doi.org/10.1109/TSC.2021.3085913

Zhang Z, Song X, Liu L, Yin J, Wang Y, Lan D (2021b) Recent advances in blockchain and artificial intelligence integration: feasibility analysis, research issues, applications, challenges, and future work. Secur Commun Netw. https://doi.org/10.1155/2021/9991535

Zhang Y, Weng Y, Lund J (2022) Applications of explainable artificial intelligence in diagnosis and surgery. Diagnostics 12(2):237. https://doi.org/10.3390/diagnostics12020237

Zhang K, Jacobsen HA (2018) Towards dependable, scalable, and pervasive distributed ledgers with blockchains (Technical Report). https://doi.org/10.1109/ICDCS.2018.00134

Zheng Z, Xie S, Dai H-N, Chen X, Wang H (2018) Blockchain challenges and opportunities: a survey. Int J Web Grid Serv 14(4):352–375. https://doi.org/10.1504/IJWGS.2018.095647

Zheng Z, Xie S, Dai H, Chen X, Wang H (2017) An overview of blockchain technology: architecture, consensus, and future trends. 2017 IEEE international congress on big data (BigData congress), IEEE. https://doi.org/10.1109/BigDataCongress.2017.85

Zheng Z, Dai H-N, Wu J (2019) Blockchain intelligence: when blockchain meets artificial intelligence. https://doi.org/10.48550/arXiv.1912.06485

Zhou Y, Wang F, Tang J, Nussinov R, Cheng F (2020) Artificial intelligence in COVID-19 drug repurposing. Lancet Digital Health 2(12):667–676. https://doi.org/10.1016/S2589-7500(20)30192-8

Zhou S, Li K, Chen Y, Yang C, Liang W, Zomaya AY (2024) TrustBCFL: mitigating data bias in IoT through blockchain-enabled federated learning. IEEE Internet Things J. https://doi.org/10.1109/JIOT.2024.3379363

Zhu J, Cao J, Saxena D, Jiang S, Ferradi H (2023) Blockchain-empowered federated learning: challenges, solutions, and future directions. ACM Comput Surv 55(11):1–31. https://doi.org/10.1145/3570953

Zhuang Y, Sheets LR, Chen Y-W, Shae Z-Y, Tsai JJ, Shyu C-R (2020) A patient-centric health information exchange framework using blockchain technology. IEEE J Biomed Health Inform 24(8):2169–2176. https://doi.org/10.1109/JBHI.2020.2993072

Zhuang Y, Sheets LR, Shae Z, Chen YW, Tsai JJ, Shyu CR (2019) Applying blockchain technology to enhance clinical trial recruitment. AMIA Annual Symposium Proceedings, American Medical Informatics Association

Živić N, Kadušić E, Kadušić K (2020). Directed acyclic graph as hashgraph: an alternative DLT to blockchains and tangles. 2020 19th International Symposium INFOTEH-JAHORINA (INFOTEH), IEEE. https://doi.org/10.1109/INFOTEH48170.2020.9066312

Zou W, Lo D, Kochhar PS, Le X-BD, Xia X, Feng Y, Chen Z, Xu B (2019) Smart contract development: challenges and opportunities. IEEE Trans Softw Eng 47(10):2084–2106. https://doi.org/10.1109/TSE.2019.2942301

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Archana Bathula

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Suneet K. Gupta

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Suresh Merugu

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Conceptualization: J.S.S., A.B., and S.G., Literature Review and Theoretical Framework: J.S.S., A.B., and S.M., Methodological Framework for Review: J.S.S., S.S.S and L.S. Critical Analysis and Synthesis of Literature: J.S.S., A.B., L.S., J.R.L.,R.S and N.N.K Evaluation of Existing Models and Theories: J.S.S., S.S.S., L.S., S.M., D.G,and S.G. Identification of Research Gaps: J.S.S., R.S, A.B., and S.M.Compilation and Organization of Literature: J.S.S., S.M, L.S., N.N.K., M.M.F and A.B., Validation of Review Findings: J.S.S., S.S.S and L.S.and N.N.K.Critical Writing and Interpretation of Findings: J.S.S., A.B., M.A.M., L.S., M.M.F, and S.M.Discussion of Implications and Future Directions: J.S.S., and S.G.Writing-Review Manuscript: J.S.S., A.B., S.G, Editing and Revision of Manuscript: J.S.S., S.S.S., L.S., D.G, and S.G., Overall Supervision and Guidance: J.S.S., S.M., D.G and S.G., All authors have read and agreed to the published version of the manuscript.

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Abbreviations adopted in AI-enabled blockchain articles in healthcare system design

• *CNSC: Capsule Network based segmentation and classification
• #CDML: Chained distributed machine learning; Blockchain AITSS: Blockchain and AI-empowered telesurgery system; HC: Healthcare.

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Bathula, A., Gupta, S.K., Merugu, S. et al. Blockchain, artificial intelligence, and healthcare: the tripod of future—a narrative review. Artif Intell Rev 57 , 238 (2024). https://doi.org/10.1007/s10462-024-10873-5

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Published : 08 August 2024

DOI : https://doi.org/10.1007/s10462-024-10873-5

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