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. AI has revolutionized the medical field, powering intelligent systems that assist in diagnostics, treatment planning, risk prediction, and patient management. However, as these models grow increasingly complex, their "black-box" nature has raised critical concerns about trust, transparency, accountability, and clinical adoption. Medical professionals, researchers, and patients alike are now demanding systems that not only perform with high accuracy but also offer clear explanations behind their decisions. This has led to a pressing need for Explainable Artificial Intelligence (XAI) in healthcare-a field where every decision can significantly impact human life. This 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 policy makers with the knowledge needed to evaluate, adopt, and trust AI solutions in critical medical applications. The 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. By reading this book, readers will gain a practical and theoretical understanding of XAI concepts, tools, and techniques tailored for medical decision support. It will help them design systems that are not only accurate and efficient but also interpretable, transparent, and aligned with human-centric values. 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