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.

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

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