Explainable AI for Transparent and Trustworthy Medical Decision Support

Abhishek Kumar is Assistant Director and Professor in the Department of Computer Science and Engineering at Chandigarh University, Punjab, India. He holds a Ph.D. in Computer Science from the University of Madras and is currently a Post-Doctoral Fellow with the Ingenium Research Group, Universidad de Castilla-La Mancha, Ciudad Real, Spain. He received his M.Tech in Computer Science and Engineering and B.Tech in Information Technology from Rajasthan Technical University, Kota, India. He has over thirteen years of academic teaching experience. His research interests include artificial intelligence, computer vision, image processing, data mining, machine learning, and renewable energy systems. He has authored and edited several books with leading international publishers and serves as a reviewer for reputed journals. Dr. C. Dhaya is currently working as a Professor and Head in Computer Science and Engineering in Adhiparasakthi Engineering College, Melmaruvathur, Tamilnadu, India. She received her Bachelor’s degree from Madras University, Master’s degree from Anna University and Doctorate degree from Pondicherry University. She has published more than twenty research papers in reputed International Journals and Conferences and published patents. Her areas of specialization include Machine Learning, Data Science & Big Data, Software Architecture Evaluation, Genetic Algorithms and Multi criteria decision making. She served as a reviewer for Elsevier, ETRI and some more reputed journals. She is a Life Member of CSI and ISTE. Her dedication to excellence in academia has been recognized through various awards and accolades, including the "Women Leadership Award" by the Computer Society of India and the "Young Researcher Award" for contributions to Science and Technology. Dr Reyes Juárez Ramírez is a Full Professor of Computer Science at the Autonomous University of Baja California, Tijuana, Mexico. He currently serves as President of the Mexican Network of Software Engineering and is a Level 2 member of Mexico’s National System of Researchers. He leads several industry-linked research projects and specializes in applying data science to software engineering. His work focuses on uncertainty in agile methodologies, quality enhancement in Scrum, user-centered design, adaptive interfaces, and emerging research in quantum computing. He has also served as General Chair for the National and International Conference on Software Engineering Research and Innovation. Angeles is Doctorate in Sciences from Autonomous University of Baja California, Master's degree in Computer Science from the Technological Institute of Tijuana, Bachelor's degree in Computer Science from the Technological Institute of Tapachula, Chiapas. She is currently a research professor in the Master's Degree in Information Technologies at the Tijuana Technological Institute, where she participates in research projects and teaching. She is the author of various scientific publications including indexed journals, book chapters and conference articles. She is a member of the National System of Researchers SNI level 1 and a member of the Mexican Thematic Network of Software Engineering (REDMIS). Research areas include Human Computer Interaction, Artificial Intelligence and Machine Learning.Dr. Pramod Singh Rathore is currently an Assistant Professor in the Department of Computer and Communication Engineering at Manipal University Jaipur, in India. He completed his PhD in computer science and engineering at the University of Engineering and Management (UEM), Jaipur, India. With over 12 years of academic teaching experience, he has more than 85 publications in peer-reviewed national and international journals, books, and conferences. He has co-authored or co-edited numerous books with well-known publishers. Dr. Singh Rathore’s research interests include NS2, computer networks, mining, and DBMS. He serves on the editorial and advisory committees of the Global Journal Group and is also a member of various national and international professional societies in the fields of engineering and research, including the ACM and International Association of Engineers (IAENG).

• Part I. Foundations of Explainable AI in Medicine1. Introduction to Explainable Artificial Intelligence (XAI)2. The Need for Transparency in Medical AI Systems3. Ethical and Legal Dimensions of AI in Healthcare4. Trust, Accountability, and Human-in-the-Loop Decision MakingPart II. XAI Techniques and Methods5. Interpretable vs. Explainable Models. A Practical Overview6. Model-Agnostic XAI Methods. LIME, SHAP, and Beyond7. Visual Explanation Techniques for Medical Imaging8. Attention Mechanisms and Feature Importance in Deep Learning9. Emerging Trends in Explainable AI for Genomics and PathologyPart III. Applications in Medical Decision Support10. Explainable AI in Radiology and Medical Imaging11. XAI for Predictive Modeling in Electronic Health Records (EHRs)12. Transparent AI for Disease Diagnosis and Prognosis13. Case Studies. Trustworthy AI in COVID-19 and Cancer DetectionPart IV. Design, Implementation, and Evaluation14. Building Trust-Centered AI Systems in Clinical Settings15. User-Centered Design for Clinician-Friendly Explanations16. Evaluating Explanation Effectiveness in Healthcare. Metrics, Benchmarks, and Methodologies for XAI17. Regulatory Standards and Comparative Frameworks for Explainable AI in MedicinePart V. Future Directions and Challenges18. Personalized Explanations and Adaptive Decision Support19. Challenges in Deploying XAI at Scale in Healthcare20. The Future of Human-AI Collaboration in Medical Practice