AI/ML for Healthcare : Navigating the AI/ML Maze Responsibly, Securely, and Sustainably

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 questions: 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.