AI foundations
Explain what common AI systems can and cannot do.
- Machine learning and generative AI
- Data, models and outputs
- Limitations and uncertainty
AI for DoctorsInstituteExplore coursesAI competency framework
Learn AI as a clinical responsibility.
Six domains connect technical literacy with evidence, safety, governance, workflow and human communication.
The framework is designed for professional development and informed by emerging priorities in medical education. It is not an accreditation standard and does not certify competence.
Evidence and clinical evaluation
Evaluate whether evidence and performance transfer to the intended patients and workflow.
- Validation and comparators
- Calibration and subgroup performance
- Clinical outcomes and usability
Privacy, data and security
Recognize how data provenance, consent and security affect safe AI use.
- Minimum-necessary data
- Patient consent and equity
- Security and data governance
Ethics, bias and governance
Participate in accountable oversight of AI-supported care.
- Fairness and health equity
- Transparency and accountability
- Policy and lifecycle monitoring
Workflow and implementation
Map human oversight, escalation, failure modes and operational impact.
- Intended use and users
- Human factors and automation bias
- Monitoring and change control
Communication and leadership
Lead teams and communicate responsibly with patients and colleagues.
- Patient-facing transparency
- Interprofessional education
- Change leadership and safety culture
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