AI/ML for Healthcare (eBook, PDF)
Navigating the AI/ML Maze Responsibly, Securely, and Sustainably
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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/machine learning (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) ...
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/machine learning (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. This 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 and securing AI/ML systems).
This 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 Machine Learning Operations (MLOps) processes and launching citizen data science programs. At its heart, this 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 this 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, this book emphasizes the importance of adopting "reciprocal altruism" to accelerate AI/ML value realization across the healthcare industry and provides practical recommendations toward the same.
This 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 Machine Learning Operations (MLOps) processes and launching citizen data science programs. At its heart, this 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 this 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, this book emphasizes the importance of adopting "reciprocal altruism" to accelerate AI/ML value realization across the healthcare industry and provides practical recommendations toward the same.
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