Explainable AI for Transparent and Trustworthy Medical Decision Support
- 1st Edition - September 1, 2026
- Latest edition
- Editors: Abhishek Kumar, Dhaya Chinnathambi, Reyes Juárez Ramírez, Angeles Quezada, Pramod Singh Rathore
- Language: English
Explainable AI for Transparent and Trustworthy Medical Decision Support equips readers with a comprehensive and timely resource that presents the principles, methodologies, and re… Read more
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It is designed to empower not only AI researchers and developers but also healthcare administrators and policymakers with the knowledge needed to evaluate, adopt, and trust AI solutions in critical medical applications. The book's authors bring together theory, implementation strategies, ethical implications, and case studies under one cover, offering a multidisciplinary perspective that aligns computer science with medical practice and healthcare policy.
- Presents detailed coverage of XAI methods such as SHAP, LIME, and Grad-CAM applied to medical data
- Provides numerous case studies in diagnostics, ICU prediction, and radiology using explainable models
- Includes discussions on ethics, bias, and regulatory frameworks such as GDPR and HIPAA
1. Introduction to Explainable Artificial Intelligence (XAI)
2. The Need for Transparency in Medical AI Systems
3. Ethical and Legal Dimensions of AI in Healthcare
4. Trust, Accountability, and Human-in-the-Loop Decision Making
Part II. XAI Techniques and Methods
5. Interpretable vs. Explainable Models. A Practical Overview
6. Model-Agnostic XAI Methods. LIME, SHAP, and Beyond
7. Visual Explanation Techniques for Medical Imaging
8. Attention Mechanisms and Feature Importance in Deep Learning
9. Emerging Trends in Explainable AI for Genomics and Pathology
Part III. Applications in Medical Decision Support
10. Explainable AI in Radiology and Medical Imaging
11. XAI for Predictive Modeling in Electronic Health Records (EHRs)
12. Transparent AI for Disease Diagnosis and Prognosis
13. Case Studies. Trustworthy AI in COVID-19 and Cancer Detection
Part IV. Design, Implementation, and Evaluation
14. Building Trust-Centered AI Systems in Clinical Settings
15. User-Centered Design for Clinician-Friendly Explanations
16. Evaluating Explanation Effectiveness in Healthcare. Metrics, Benchmarks, and Methodologies for XAI
17. Regulatory Standards and Comparative Frameworks for Explainable AI in Medicine
Part V. Future Directions and Challenges
18. Personalized Explanations and Adaptive Decision Support
19. Challenges in Deploying XAI at Scale in Healthcare
20. The Future of Human-AI Collaboration in Medical Practice
- Edition: 1
- Latest edition
- Published: September 1, 2026
- Language: English
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Abhishek Kumar
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Dhaya Chinnathambi
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Reyes Juárez Ramírez
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Angeles Quezada
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.
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Pramod Singh Rathore
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).