Responsible and Explainable Artificial Intelligence in Healthcare

Ethics and Transparency at the Intersection

1st Edition - November 14, 2024
Editors: Akansha Singh, Krishna Kant Singh, Ivan Izonin
Language: English
Paperback ISBN:
9 7 8 - 0 - 4 4 3 - 2 4 7 8 8 - 0
eBook ISBN:
9 7 8 - 0 - 4 4 3 - 2 4 7 8 9 - 7

Responsible and Explainable Artificial Intelligence in Healthcare: Ethics and Transparency at the Intersection provides clear guidance on building trustworthy Artificial Intell… Read more

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Responsible and Explainable Artificial Intelligence in Healthcare: Ethics and Transparency at the Intersection provides clear guidance on building trustworthy Artificial Intelligence systems for healthcare. The book focuses on using Artificial Intelligence to improve diagnosis, prevent diseases, and personalize patient care. It addresses potential drawbacks, like reduced human interaction and ethical concerns, offering solutions for ethical and transparent Artificial Intelligence use in medicine. Across eight chapters, the book explores Artificial Intelligence's current status, its importance, and associated risks in healthcare. It explains designing reliable Artificial Intelligence for healthcare, tackling biases, and safeguarding patient privacy in the age of big data. The legal and regulatory landscape is also covered. One chapter is dedicated to showcasing real-world examples of responsible Artificial Intelligence in healthcare, highlighting best practices. The book concludes by summarizing key takeaways and discussing future challenges. "Responsible and Explainable Artificial Intelligence in Healthcare: Ethics and Transparency at the Intersection" is a valuable resource for healthcare professionals, policymakers, computer scientists, and ethicists concerned about Artificial Intelligence's ethical and societal impact on medicine.

Title of Book
Cover image
Title page
Table of Contents
Copyright
Contributors
Chapter 1. Revolutionizing healthcare: The transformative role of artificial intelligence
Intoduction
Artificial Intelligence
Expert systems
Fuzzy logic
Artificial neural networks
Machine learning
Natural language processing
Computer vision
Robotics
Deep learning
Healthcare
Artificial intelligence today
Medium-term AI
Long-term
Connected/augmented care
Precision diagnostics
Precision therapeutics
Precision medicine
International trends
Stages of artificial intelligence in medical procedures
Locating the problematic region that requires intervention
Eliminate the following possibilities
Clinical studies that are both more effective and faster
Developing diagnostic biomarkers in order to identify diseases
AI's potential and advantages in healthcare
Requirement of experts
Issues and challenges in the adoption of AI in healthcare
Reasonableness and imbalance
Conclusion
Chapter 2. Ethical considerations in AI powered diagnosis and treatment
Introduction
Methodology
Ethical issues
Ethical issue one
Ethical issue two
Ethical issue three
Ethical issue four
Ethical issue five
Ethical issue six
Supplementary ethical issue
Strategies for solving ethical issues
Applications of AI in the health domain
Conclusion
Chapter 3. Explainable AI methods to increase trustworthiness in healthcare
Introduction
Methodology
Data analysis and visualization
Description of the classification task
Age
Sex
Chest pain type
Resting BP
Cholesterol
Fasting BS
Resting ECG (electrocardiogram)
Max HR
Exercise angina
Oldpeak
ST slope
Data sample
Features of the data set
Explaining with data visualization
Target class
Correlation between features
Comparison of AI and XAI methods for classification task in healthcare
Types of models
Model creation
Neural network
Model comparison
Explaining with permutation feature importance and SHAP
Recursive feature selection
Decision boundary
Explaining with decision tree machine learning model
Conclusions
Chapter 4. Designing transparent and accountable AI systems for healthcare
Introduction
Exploring artificial intelligence
AI and ML: Revolutionizing healthcare
Understanding the importance of transparency in healthcare AI
Ethical considerations for AI in medical settings
Challenges of implementing AI and ML in the medical field
Navigating the ethical landscape: A review of transparent and accountable AI systems in healthcare
Applications of AI in healthcare: Enhancing transparency and accountability
Accountability, transparency and explainability in AI for healthcare
Understanding explainability in cardiac arrest prediction tools
Unlocking the Black Box: exploring interpretability methods for deep neural networks in medical image analysis
AI-aided drug discovery efforts
Transforming healthcare: Leveraging artificial intelligence in clinical practice
The use of AI in healthcare: Past, present, and future
Frameworks for designing transparent AI systems
Enhancing biomedical data sharing and privacy framework
Condensing and rephrasing the ethical and responsible practices framework
Accountability and transparency framework
Conclusion
Chapter 5. Ensuring fairness and mitigating bias in healthcare AI systems
Introduction to fairness in healthcare AI
The role of AI in healthcare
The promise of AI: Enhancements in patient care
The challenge of bias: Risks and consequences
The importance of fair AI systems
Understanding bias in AI
Types of bias in AI systems
Sources of bias in healthcare AI systems
Identifying sources of bias
Impact of bias
Strategies for mitigating bias
Fairness in AI
Measuring fairness in AI systems
Implementing fairness in AI systems
Conclusion
Chapter 6. AI enhanced healthcare: Opportunities, challenges, ethical considerations, and future risk
Introduction
Related work
AI in healthcare
Machine learning (ML)
Natural language processing (NLP)
Expert system
Robots and robotic process
Applying AI in healthcare
Patient engagement and adherence applications
Diagnosis and treatment applications
Administrative applications
Ethical principles in AI-powered diagnosis and treatment
Autonomy
Risk reduction and safety
Privacy of data
Accountability and liability
Nondiscrimination and fairness principles
Validity
Trustworthiness
Data quality enhancement
Equity, inclusivity, and accessibility
Collaboration
Challenges of AI in healthcare
Limited data
Data bias
Lack of transparency
Lack of standardization
Lack of understanding
Security and data privacy
Fear of change and lack of trust in AI
High cost
Future of AI in health care
Conclusion
Chapter 7. Healthcare revolution: Advances in AI-driven medical imaging and diagnosis
Introduction
Technological advancements
AI in healthcare
Artificial intelligence and medical visualization
CV for surgery and diagnosis
Healthcare using AR and VR
Patient experience
Intelligent personal health records
Health monitoring and wearables
Natural language processing
Personal records integration
Smart devices
Minimally invasive surgery
Neuroprosthetics
Ambient assisted living
Smart home
Assistive robots
Cognitive assistants
Social and emotional stimulation
Chapter 8. A deep learning approach for medical image classification using XAI and convolutional neural networks
Introduction
State-of-the-arts
Materials and methods
Analysis of selected datasets
The proposed model architecture
Training, validation and evaluation of models
Modeling and results
Model development and analysis tools
Results of applying the proposed algorithm for Dataset 1
Results of applying the proposed algorithm for Dataset 2
Analyzing the behavior of classification models when applying XAI methods
Discussion results for Dataset 1
Discussion results for Dataset 2
Conclusions
Funding
Chapter 9. Hybrid ensemble learning model to improve the performance and interpretability of medical diagnosis: Small data tasks
Introduction
Background
Naïve Bayes
SGTM neural-like structures
Ito decomposition
Procedure for the synthesis of linear polynomial
Hybrid cascade-based prediction model and its algorithmic implementations
Design of the hybrid cascade-based ensemble learning model with improved performance and interpretability
First algorithmic implementation of the proposed method (Algorithm 1)
Second algorithmic implementation of the proposed method (Algorithm 2)
Third algorithmic implementation of the proposed method (Algorithm 3)
Datasets description and their preprocessing
Dataset 1
Dataset 2
Dataset 3
Balancing techniques for small data analysis
Modeling and results
Data balancing results
Implementation results of the Algorithm 1
Implementation results of the Algorithm 2
Implementation results of the Algorithm 3
Comparison and discussion
Comparison of the accuracy of all investigated methods
Comparison of the training duration for all investigated methods
Comparison of the interpretability of all investigated methods
Conclusions
Funding
Chapter 10. Legal and regulatory issues related to AI in healthcare
Introduction
The rise of AI in healthcare
AI in healthcare market trends
Legal and regulatory landscape
Legal and ethical problems raised
Importance of resolving legal and regulatory matters
Benefits of AI in healthcare
Challenges faced with legal issues of AI in healthcare
AI in healthcare: A gift or a threat?
Mainstream use cases involving AI's legal and regulatory concerns in healthcare
Legal and ethical concerns in the realm of reproductive health within the field of neurology
HIV-related public health, legal, and moral concerns pertaining to expectant mothers and new-borns
Juvenile justice: Legal concerns, rights, and ethics for mental health
Concerns about law and ethics in college mental health
Treating adolescent patients in AI in healthcare: Legal and ethical considerations
The social, legal, and ethical implications of AI to the treatment of breast cancer
Safeguarding patients' personal information
“Doctor ChatGPT”: LLMs and a request for regulation
Legal and regulatory issues with IOT technology and wearables
AI legal: Concerns related to a scam, waste, and exploitation prevention
Conclusion
Chapter 11. Responsible and explainable artificial intelligence in healthcare: Conclusion and future directions
Introduction
Ethical foundations
Case studies and applications
Technological advancements
Major challenges and resolutions
Data privacy and security
Bias and fairness
Regulatory frameworks
Future directions
Technological advances in AI
Interdisciplinary collaboration
Patient-centered approaches
Conclusion: Shaping the future of AI in healthcare
Call to action
Vision for the future
Index

Akansha Singh

Prof. Akansha Singh, Professor at the School of Computer Science and Engineering, Bennett University, Greater Noida, boasts a comprehensive academic background with a B.Tech, M.Tech, and Ph.D. in Computer Science. Her doctoral studies, conducted at the prestigious IIT Roorkee, were focused on the cutting-edge fields of image processing and machine learning. A prolific author and scholar, Dr. Singh has contributed over 100 research papers and penned more than 25 books. Her editorial expertise is recognized by leading publishers such as Elsevier, Taylor and Francis, and Wiley, where she has edited books on a variety of emerging topics.Dr. Singh serves as the Associate Editor in IEEE Access, Discover Applied Science, PLOS One and guest editor in several journals. Her research interests are diverse and influential, spanning image processing, remote sensing, the Internet of Things (IoT), Blockchain and machine learning. Prof. Singh’s work in these areas not only advances the field of computer science but also significantly contributes to the broader scientific and technological community.

Affiliations and expertise

School of CSET, Bennett University, Greater Noida, India

Krishna Kant Singh

Dr. Krishna Kant Singh, currently the esteemed Director of Delhi Technical Campus in Greater Noida, India, is a highly experienced educator and researcher in the field of engineering and technology. He is a B.Tech and M.Tech degree, a Postgraduate Diploma in Machine Learning and Artificial Intelligence from IIIT Bangalore, a Master of Science in Machine Learning and Artificial Intelligence from Liverpool John Moores University, United Kingdom, and a Ph.D. from IIT Roorkee. Dr. Singh has made significant contributions to the academic and research community. With over 19 years of teaching experience, he has played a vital role in educating and mentoring future professionals. Dr. Singh also serves as an Associate Editor at IEEE Access, an Editorial Board Member at Applied Computing and Geosciences (Elsevier), and a Guest Editor for Complex and Intelligent Systems. His extensive publication record includes over 132 research papers. His areas of interest include Machine Learning, Deep Learning, computer vision and so on.

Affiliations and expertise

Delhi Technical Campus, Greater Noida, India

Ivan Izonin

Dr. Ivan Izonin is an Associate Professor at the Department of Artificial Intelligence, Lviv Polytechnic National University, Ukraine. He holds a Ph.D. in computer science and has several years of experience in teaching, research, and development. Dr. Izonin's research interests include AI, healthcare, machine learning, and data mining. He has contributed to various international journals and conferences and has authored several research papers, including chapters in books. His work on text mining and natural language processing has been widely cited in the academic community. Dr. Izonin is well-respected in his field and has served as an Editor, Guest Editor, reviewer for several international journals and conferences

Affiliations and expertise

Lviv Polytechnic National University, Lviv, Ukraine

Life Sciences

Physical Sciences & Engineering

Social Sciences & Humanities

Health

Responsible and Explainable Artificial Intelligence in Healthcare

Ethics and Transparency at the Intersection

Purchase options

Institutional subscription on ScienceDirect

Akansha Singh

Krishna Kant Singh

Ivan Izonin