Explainable AI for Transparent and Trustworthy Medical Decision Support
Will be published approx. on 1. September 2026
Book
Paperback/Softback
300 pages
978-0-443-45697-8 (ISBN)
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.
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.
More details
Content
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
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