Artificial Intelligence (AI) and Machine Learning (ML) for Healthcare and Health Sciences: The Need for Best Practices Enabling Trust in AI and ML

Abstract

Abstract In the opening chapter we first introduce essential concepts about Artificial Intelligence and Machine Learning (AI/ML) in Health Care and the Health Sciences (aka Biomedical AI/ML). We then provide a brief historical perspective of the field including highlights of achievements of Biomedical AI/ML, the various generations of AI/ML efforts, and the recent explosive interest in such methods and future growth expectations. We summarize how biomedical AI and ML differ from general-purpose AI/ML. We show that pitfalls and related lack of best practices undermine practice and potential of Biomedical AI/ML. We introduce high-level requirements for biomedical AI/ML and 7 dimensions of trust, acceptance and ultimately adoption, which serve as the driving principles of the present volume. We outline the contents of the volume, both overall and chapter-by-chapter, noting the interconnections. We discuss the intended audience, and differences from other AI/ML books. We finally discuss format, style/tone, and state a few important caveats and disclosures.

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