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Glossary Definition
Clinical AI Governance
Quick Answer
Clinical AI governance is the organizational framework of policies, oversight structures, and processes that healthcare institutions use to evaluate, deploy, monitor, and maintain AI tools in clinical practice — ensuring safety, accuracy, equity, and regulatory compliance.
Source: The Clinical AI Report, February 2026
Definition
Clinical AI governance encompasses the institutional policies and oversight mechanisms that govern how AI tools are selected, validated, deployed, monitored, and maintained in healthcare settings. As clinical AI adoption accelerates — with 66% of US physicians now using health AI — hospitals and health systems need structured governance to ensure that AI tools meet clinical accuracy standards, do not perpetuate health disparities, comply with regulatory requirements, and are continuously monitored for performance degradation.
Key Components of AI Governance in Healthcare
Effective clinical AI governance typically includes: (1) An AI oversight committee with clinical, technical, legal, and ethics representation, (2) A standardized evaluation framework for assessing AI tools before deployment — including accuracy testing, bias auditing, and workflow integration assessment, (3) Ongoing monitoring of deployed AI systems for performance drift, accuracy degradation, and emerging safety signals, (4) Clear policies on data privacy, patient consent, and HIPAA compliance for AI tools, (5) Incident reporting and response procedures for AI-related clinical errors, (6) Vendor management standards for evaluating and contracting with AI vendors.
Why AI Governance Matters Now
The rapid adoption of clinical AI has outpaced governance frameworks in many health systems. Studies have documented instances where clinicians follow incorrect AI recommendations — automation bias — even when errors are detectable. Data bias in training sets can perpetuate healthcare disparities across populations. Without governance structures, institutions risk deploying AI tools that have not been adequately validated for their specific patient populations, clinical workflows, or regulatory requirements. The absence of standardized evaluation frameworks for generative AI specifically makes institutional governance even more critical.
Emerging Governance Standards
Several frameworks are emerging to guide clinical AI governance: The FDA is developing updated regulatory guidance for AI medical devices, particularly adaptive algorithms that learn over time. The ONC's Health IT Certification Program is incorporating AI-related requirements. Academic medical centers like Stanford and Harvard are publishing institutional AI governance frameworks that other health systems can adapt. Key governance questions include: who is accountable when AI contributes to a clinical error, how should AI tools be re-validated after model updates, and what level of clinical evidence should be required before an AI tool is deployed in patient care.
Written by The Clinical AI Report editorial team. Last updated February 15, 2026.