This book discusses explainable Artificial Intelligence (AI) and its applications in healthcare, providing a broad overview of state-of-the-art approaches for accurate analysis and diagnosis. It encompasses computational vision processing techniques that handle complex physiological information, electronic healthcare records, and medical imaging data that assist in earlier prediction. This book explores how explainable AI methods provide a solution for the future of medical data analytics precision medicine and highlights the challenges and considerations that must be addressed.

This book summarizes and categorize the explainable AI types and highlight the algorithms used to increase interpretability in medical data and imaging topics. In addition, it focuses on the challenging explainable AI problems in medical applications and provide guidelines to develop better learning models using explainable AI concepts in medical image and text analysis. Furthermore, this edited book will provide future directions to guide developers and researchers for future prospective investigations on clinical topics, particularly on applications with medical data/imaging.