The integration of artificial intelligence (AI) in healthcare organizations is no longer a futuristic concept—it’s here, reshaping patient care, optimizing operational efficiency, and enhancing diagnostic accuracy. However, while AI offers immense potential, finding its practical applications, and scaling its implementation is fraught with both technical and practical challenges, ranging from data security, ethical concerns, regulatory practices, and workforce adaptation. For digital health leaders, adopting AI successfully requires a deep understanding of not only the technology itself but also the organizational and human factors that can make or break its success. Artificial Intelligence Driven Innovations in Healthcare is a practical guide designed to help digital health and IT professionals, chief medical information officers, administrators, and policymakers navigate these complexities and leverage AI to achieve sustainable improvements in care delivery.
This book is not a technical manual on AI algorithms, but rather, a guide to show how technology leaders are adopting AI within their own healthcare organization or healthcare practice. It presents best practices from the Healthcare Information Management and Systems Society (HIMSS)/INFORMA annual conferences in recent years. The includes contributions from approximately 20 presenters from HIMSS Global Health Conference. The focus of these chapters will be around AI technology’s place in helping to create innovations in operations, design, and delivery. This book will draw on real-world case studies, spotlighting successful attempts to implement AI in a range of healthcare environments, from small clinics to major hospital systems.

The book will begin with an overview of AI’s role in healthcare and its key applications, such as predictive analytics, personalized treatment planning, robotic surgery, and administrative automation. Each chapter will also feature case studies and expert insights from leading healthcare organizations that have successfully navigated the AI adoption process. These examples will provide practical lessons and real-life scenarios to help readers anticipate challenges and proactively design solutions. Each chapter will be organized into similar sections for each organization’s case study: Strategy and Vision for AI, Implementation Best Practices, Initial Results, Challenges and Future Opportunities.