AI Safety in Healthcare Systems : Resilience, Transparency, and Accountability

The book explores real-world applications of explainable AI (XAI) safely in healthcare, focusing on disease diagnosis, treatment planning, and patient management, while demonstrating how XAI enhances clinical decision-making and patient outcomes.

This book provides an in-depth exploration of AI safety in healthcare. It bridges the gap between technical innovation and patient protection. It examines emerging methodologies for risk management, transparency, and robust AI system design used specifically in medical applications. Readers will gain insights into the ethical considerations that surround AI deployment, techniques for improving the explainability and accountability of AI models, and the implementation of safety protocols to detect and prevent AI failures. The book also discusses the evolving regulatory view that shapes AI adoption and how privacy-preserving technologies can enable safer data sharing across institutions. In highlighting both current challenges and future directions, the book makes a meaningful contribution to the safe, responsible, and trustworthy integration of AI in healthcare systems. This is an illuminating read for researchers, practitioners, policymakers, and healthcare providers interested in acquiring the knowledge needed to harness AI’s potential while safeguarding human health and well-being.