AI Governance Frameworks

Compare healthcare AI frameworks by their purpose, scope and role in clinical practice.

Doctor reviewing AI-assisted healthcare data on a digital tablet

Framework map

Five authorities, five different jobs

Responsible programs combine clinical ethics, risk management, quality systems, and the law that applies in each market. This comparison is educational and does not provide legal or regulatory advice.

WHO

Ethics and governance principles for protecting autonomy, safety, transparency, accountability, equity, and sustainability.

Primary guidance ↗

NIST AI RMF

A voluntary risk management structure organized around Govern, Map, Measure, and Manage.

Primary guidance ↗

FDA

Relevant when software functions may meet the definition of a regulated medical device in the United States.

FDA overview ↗

AMA

Physician centered principles for augmented intelligence, clinical responsibility, transparency, and equitable use.

AMA resources ↗

EU AI Act

A binding, risk based European law. Duties depend on the system, role, use, and implementation timetable; obtain qualified counsel for applicability.

European Commission ↗

Translate principles into controls

DecisionMinimum evidenceOperational control
Should we use it?Intended use, affected population, alternatives, expected benefitNamed executive and clinical owners
Is it safe enough?Local validation, subgroup performance, known failure modesHuman review and escalation thresholds
Is it still working?Outcome, drift, override, complaint, and incident trendsMonitoring cadence, change control, pause authority

Ask for concise conclusions, uncertainty, supporting sources, and the information needed to verify the result. Do not treat hidden reasoning as clinical evidence.

Apply the lifecycle gates →
Selection Guide

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Discuss your intended use, clinical workflow and jurisdiction to identify relevant guidance and the questions that require specialist review.

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