Strategic and Ethical Integration of AI in Hospitals explores the transformative role of artificial intelligence (AI) in modern healthcare. It examines the adoption, implementation, and regulation of AI in hospital settings, balancing technological innovation with clinical practice, operational management, and ethical governance. Through systematic literature reviews, conceptual modelling, and case studies from hospitals worldwide, the book provides actionable insights into how AI can enhance patient care, optimize workflows, and drive strategic decision- making. Key features include a multi- perspective approach integrating clinical, organizational, ethical, and regulatory dimensions of AI adoption. The book combines theoretical frameworks with real- world examples, offering practical strategies for navigating the complexities of AI implementation. It addresses critical issues such as data privacy, patient trust, algorithmic bias, and regional disparities, ensuring relevance across diverse healthcare contexts.

This book is ideal for academics, postgraduate students, researchers, healthcare managers, and policymakers seeking rigorous analysis and practical guidance on AI in hospitals. It is particularly relevant for institutions and regions embarking on AI adoption, offering a roadmap for responsible integration that prioritizes human capability and ethical governance.

Strategic and Ethical Integration of AI in Hospitals explores the transformative role of artificial intelligence (AI) in modern healthcare. It examines the adoption, implementation, and regulation of AI in hospital settings, balancing technological innovation with clinical practice, operational management, and ethical governance. Through systematic literature reviews, conceptual modelling, and case studies from hospitals worldwide, the book provides actionable insights into how AI can enhance patient care, optimize workflows, and drive strategic decision- making. Key features include a multi- perspective approach integrating clinical, organizational, ethical, and regulatory dimensions of AI adoption. The book combines theoretical frameworks with real- world examples, offering practical strategies for navigating the complexities of AI implementation. It addresses critical issues such as data privacy, patient trust, algorithmic bias, and regional disparities, ensuring relevance across diverse healthcare contexts.

This book is ideal for academics, postgraduate students, researchers, healthcare managers, and policymakers seeking rigorous analysis and practical guidance on AI in hospitals. It is particularly relevant for institutions and regions embarking on AI adoption, offering a roadmap for responsible integration that prioritizes human capability and ethical governance.