This book combines technology and the medical domain. It covers advances in computer vision (CV) and machine learning (ML) that facilitate automation in diagnostics and therapeutic and preventive health care. The special focus on eXplainable Artificial Intelligence (XAI) uncovers the black box of ML and bridges the semantic gap between the technologists and the medical fraternity. Explainable AI in Healthcare: Unboxing Machine Learning for Biomedicine intends to be a premier reference for practitioners, researchers, and students at basic, intermediary levels and expert levels in computer science, electronics and communications, information technology, instrumentation and control, and electrical engineering.

This book will benefit readers in the following ways:

Explores state of art in computer vision and deep learning in tandem to develop autonomous or semi-autonomous algorithms for diagnosis in health care

Investigates bridges between computer scientists and physicians being built with XAI

Focuses on how data analysis provides the rationale to deal with the challenges of healthcare and making decision-making more transparent

Initiates discussions on human-AI relationships in health care

Unites learning for privacy preservation in health care

About the Author

Mehul S Raval, Associate Dean – Experiential Learning and Professor, School of Engineering and Applied Science, Ahmedabad University, Ahmedabad, India

Mohendra Roy, Assistant Professor, Information and Communication Technology Department, School of Technology, Pandit Deendayal Energy University, Gandhinagar, India

Tolga Kaya, , Professor and Director of Engineering Programs, Sacred Heart University, Fairfield, CT, USA

Rupal Kapdi, Assistant Professor, Computer Science and Engineering Department, Institute of Technology, Nirma University, Ahmedabad, India

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