Thinking Through Healthcare AI: Frameworks for Viable Innovation

About this book

"Creating novel solutions in healthcare has historically been a complex, daunting undertaking. In recent years, exciting breakthroughs in predictive algorithms and large language models—artificial intelligence, writ large—have opened new horizons in healthcare. On the other hand, AI is at least as challenging to manage as it is promising when human health and wellbeing are at stake. Emily has brought together a valuable playbook that identifies and helps address the most common challenges AI-driven product innovation faces in healthcare. Seasoned healthcare veterans and new entrants alike will benefit from this book."—Bill Evans, Founder and General Partner, Rock Health CapitalMost healthcare AI projects do not fail because the model is unusable. They fail because they do not fit clinical workflows, regulatory requirements, reimbursement structures, or the operational realities of care delivery.Thinking Through Healthcare AI is a practical guide for students, technologists, clinicians, and operators who want to understand how AI actually works in healthcare settings. It focuses not on building models in isolation, but on making systems that can be deployed, trusted, and sustained.Inside, you’ll learn how to think about feature engineering, model selection, threshold tuning, validation, equity, and workflow integration through real-world examples and case-based explanations. The emphasis is on decision-making: what matters, what doesn’t, and how to avoid the common failure modes that prevent otherwise promising systems from succeeding.This is not a book about hype, and it is not limited to large language models. It is a framework for evaluating and building healthcare AI systems that work under real clinical, regulatory, and operational constraints.If you want to understand what makes healthcare AI succeed or fail in practice, this book is for you. Read more