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AI/ML for Healthcare
The advent of Generative AI has democratized access to AI, prompting nearly everyone in healthcare organizations - from frontline workers to business leaders - to ask pressing How can I be better equipped to support AI adoption meaningfully? How do I ensure I ask the right questions? What cautions should I exercise as I think about AI/ML in my business process? This book aims to answer these and other such questions, and to empower healthcare professionals, at all levels, by providing them knowledge across various aspects of AI/ML, enabling them (at least in part) to realize positive, lasting business value from AI and ML initiatives. The book draws upon my experience of working in healthcare AI/ML, lessons I learned while observing leaders in this space trying to make a difference, and research (for evolving topics like sustainable AI development).
The book provides readers with actionable insights to build responsible, secure, and sustainable AI/ML solutions in healthcare and delves into key principles for scaling AI/ML value delivery including establishing MLOps processes and launching citizen data science programs. At its heart, the book features a healthcare-specific case study that bridges the gap between theoretical knowledge and practical application, illustrating all major concepts in a real-world context.
The final chapter of the book offers a forward-looking commentary on the future of healthcare AI/ML. It explores the potential of Generative AI for healthcare, and advocates leveraging lessons from past AI/ML implementations to chart a meaningful path for embracing Generative AI. Additionally, the book emphasizes the importance of adopting “reciprocal altruism” to accelerate AI/ML value realization across the healthcare industry and provides practical recommendations towards the same.
The book provides readers with actionable insights to build responsible, secure, and sustainable AI/ML solutions in healthcare and delves into key principles for scaling AI/ML value delivery including establishing MLOps processes and launching citizen data science programs. At its heart, the book features a healthcare-specific case study that bridges the gap between theoretical knowledge and practical application, illustrating all major concepts in a real-world context.
The final chapter of the book offers a forward-looking commentary on the future of healthcare AI/ML. It explores the potential of Generative AI for healthcare, and advocates leveraging lessons from past AI/ML implementations to chart a meaningful path for embracing Generative AI. Additionally, the book emphasizes the importance of adopting “reciprocal altruism” to accelerate AI/ML value realization across the healthcare industry and provides practical recommendations towards the same.
296 pages, Hardcover
Published June 8, 2025
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July 25, 2025
In today’s times of uncertainty and noise surrounding AI, this book is a very insightful read - both in terms of the step by step break down of the many facets of AI applications in the field of medicine and health care as well as answering questions around the ethical and social implications of its use. It is not only very well researched, you can tell from the thoroughness and detail that the author is speaking from her own first hand experience of having worked in this industry for many years. The writing is well balanced - professional, objective yet easy to understand. I found myself learning so much about healthcare and about AI. The book very succinctly answers any questions that come to mind as one reads it - without overwhelming or intimidating someone who is new to the world of AI. All in all, a very well researched and insightful book! A must have for any professional looking for an understanding of AI in the medical and healthcare domain.
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