Description

Generative AI is rapidly entering clinical and administrative workflows, introducing new risks to patient safety, data privacy, and regulatory compliance. Securing Generative AI in Healthcare provides a practical governance and security guide for healthcare organizations adopting AI responsibly.

This book addresses the unique risk profile of healthcare AI, where errors, bias, or data leakage can directly affect patient outcomes. It explains how generative models differ from traditional clinical systems and why existing controls are often insufficient.

Readers are guided through regulatory expectations, clinical validation requirements, and operational safeguards needed to deploy generative AI in healthcare environments. The book emphasizes practical decision-making, vendor oversight, and defensible governance rather than experimental use.

Key topics include:

Privacy and data protection for clinical AI systems Patient safety risks and mitigation strategies Clinical validation and performance monitoring Governance structures for healthcare AI oversight Vendor due diligence and contractual safeguards

Written for healthcare IT leaders and clinicians, this guide supports safe, compliant, and trustworthy use of generative AI in patient-facing and operational settings.