Explainable AI in Healthcare Imaging for Medical Diagnoses

Explainable AI in Healthcare Imaging for Medical Diagnoses by Tanzila Saba, Ahmad Taher Azar, Seifedine Kadry

Transform the way you interpret medical images with a practical, research-driven guide that brings clarity to AI decisions in radiology and clinical imaging. This book captures the urgent need for transparent, trustworthy machine learning in healthcare—essential reading for radiologists, data scientists, biomedical engineers, clinicians, and policy makers across hospitals and research centers worldwide.

Inside, you’ll find clear explanations of interpretability techniques, from saliency maps and attention mechanisms to model-agnostic methods and explainable deep learning architectures, all framed around real-world imaging modalities (X-ray, CT, MRI, ultrasound). The authors balance technical depth with clinical relevance: evaluation metrics, case studies, and deployment considerations show how explainability improves diagnostic confidence, patient safety, and regulatory readiness in regions across North America, Europe, and Asia.

Whether you’re developing clinical decision-support tools or validating AI for hospital use, this volume bridges the gap between complex algorithms and actionable medical insight. Learn how to design transparent models, communicate risk to clinicians, and integrate interpretability into workflows to increase adoption and trust.

Practical, evidence-based, and globally relevant, this book equips professionals and students to build explainable AI systems that meet ethical and regulatory demands while enhancing diagnostic accuracy. Add Explainable AI in Healthcare Imaging for Medical Diagnoses to your library to lead the next wave of responsible AI in medical imaging—empower clinical teams with tools they can understand and trust.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.