AI Assurance in Healthcare: Foundations, Leading Frameworks, and Future Directions

Abstract

As artificial intelligence (AI) moves into clinical workflows, health systems need clear ways to ensure models are safe, effective, fair, and trustworthy across their life cycle.AI assurance provides that structure through governance, evaluation, documentation, and ongoing monitoring.This review summarizes core principles for health care assurance, highlights leading frameworks used in practice, and outlines nearterm priorities for implementers.We map areas of alignment across the Coalition for Health AI guidance, the NIST AI Risk Management Framework, Korea's trustworthy AI guidance, the European Union's AI Act, and the World Health Organization's ethics guidance, then describe what each contributes for clinical deployment.We close with practical next steps for health systems, including fit-for-purpose reporting, shift detection and post market monitoring, and federated approaches that respect data sovereignty while enabling evaluation at scale.The goal is a concise primer that helps technical and clinical leaders implement reliable AI in real settings.

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