Resource pillar · Healthcare AI education
Healthcare AI education: from concepts to responsible use
Healthcare AI education should explain capabilities and limitations in the setting where learners work. Practical use requires clinical judgement and oversight.
Start with audience and clinical context
Identify whether learners need introductory AI literacy, workflow evaluation or supervised tool-building.
Select examples appropriate to their role. Use approved or synthetic material rather than identifiable patient information in teaching examples.
Teach limitations alongside use cases
Discuss unsupported answers, source quality, bias and data handling. Make escalation and human oversight explicit.
WHO's guidance addresses ethical and governance considerations for AI in health. It does not endorse Perceptors AI.
Assess application, not only recall
Ask learners to explain why an AI output can or cannot be used in a given situation.
Document the assessment criteria. Do not infer clinical safety or patient outcomes from a completion certificate.
Articles in the Healthcare AI education cluster
Read the supporting articles, then return here for the wider framework.
- Workshop: Building an agentic AI tutor for biomedical and life sciences
- One million AI-ready healthcare professionals: GCLS and Parallaxnet scale agentic learning
- GCLS AI Lab partners with UBC DASH for Vibe Coding for Healthcare 101
- Building Agentic AI Tutor for Biomedical and Life Sciences
- Healthcare Leadership Development: Online Program Options
- AI for Medical Education: How Intelligent Agents Are Changing Clinical Learning
- Healthcare Onboarding Software: What Clinical Teams Should Look for in a Platform