Название: Explainable AI in Healthcare: Unboxing Machine Learning for BiomedicineАвтор: Mehul S Raval, Mohendra Roy, Tolga Kaya
Издательство: CRC Press
Год: 2024
Страниц: 329
Язык: английский
Формат: pdf (true)
Размер: 10.6 MB
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.
The book will focus on the following two broad sections: Section I – Introduction and Preprocessing and Section II – Use Cases of XAI in Healthcare. The book commences with a chapter on “Human–AI Relationship in Healthcare.” It discusses the hurdles in adopting AI algorithms in the field of medicine even though AI algorithms are better or match human capabilities. It highlights the need to better understand human–AI relationships and interactions. It suggests viewing AI as an assistant from whom humans can seek and give feedback over time. The second chapter “Deep Learning in Medical Image Analysis: Recent Models and Explainability” lays the foundation and reviews recent Deep Learning (DL) algorithms for medical image analysis. It showcases implementations in critical applications. It then introduces XAI techniques for DL methods and discusses the challenges in this domain. Imaging techniques provide a wealth of information in clinical applications.
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
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