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Elsevier

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

Description

Explainable AI for Transparent and Trustworthy Medical Decision Support equips readers with a comprehensive and timely resource that presents the principles, methodologies, and real-world applications of explainable AI (XAI) within the medical context. Covering a wide range of use cases—from radiology and pathology to genomics and clinical decision support systems—the book provides in-depth discussions on how XAI techniques can enhance interpretability, improve clinician trust, meet regulatory requirements, and ultimately lead to better patient outcomes. The book demystifies the workings of machine learning models and highlights techniques that make them interpretable.

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.

Key features

  • 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

Readership

Computer Scientists and researchers in Artificial Intelligence and Machine Learning, as well as Ph.D. scholars and academic researchers working in the fields of Biomedical Engineering, Medical Informatics, and Health Data Science. The audience also includes healthcare AI professionals and data scientists who are developing explainable models for clinical decision-making and diagnostic support

Table of contents

Part I. Foundations of Explainable AI in Medicine

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

Product details

  • Edition: 1
  • Latest edition
  • Published: September 1, 2026
  • Language: English

About the editors

AK

Abhishek Kumar

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.

Affiliations and expertise
Chandigarh University, Punjab, India

DC

Dhaya Chinnathambi

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.
Affiliations and expertise
Department of Computer Science & Engineering, Adhiparasakthi Engineering College, Melmaruvathur, India

RR

Reyes Juárez Ramírez

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.
Affiliations and expertise
Autonomous University of Baja California, Mexicali, Mexico

AQ

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.

Affiliations and expertise
Department of Systems and Computing, Institute of Tijuana, Baja California, Mexico

PS

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).

Affiliations and expertise
Department of Computer and Communication Engineering, Manipal University, Jaipur, India