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How to Teach Generative Artificial Intelligence in Undergraduate Medical Education
Eva Feigerlova1,2,3
1Centre de Référence des Maladies Héréditaires du Métabolisme, Centre Hospitalier Universitaire de Nancy, Nancy, France.
The Clinical Teacher
|April 9, 2026
Summary
Generative artificial intelligence (AI) offers healthcare benefits but requires training. This study proposes a practical framework for medical education to ensure safe and responsible use of AI tools by future clinicians.
Area of Science:
- Medical Education
- Artificial Intelligence in Healthcare
- Clinical Decision Support
Background:
- Generative artificial intelligence (AI) systems analyze data and generate outputs to aid decision-making.
- AI integration in healthcare promises enhanced diagnostic accuracy and clinical decision support.
- Medical curricula lag behind AI adoption, resulting in inconsistent AI education for students.
Purpose of the Study:
- To address the gap in AI education within medical curricula.
- To provide a pragmatic framework for teaching medical students the practical application of generative AI.
- To prepare future clinicians for safe and responsible engagement with AI in clinical practice.
Main Methods:
- Integrating AI verification, critical appraisal, and ethical reflection into clinical teaching.
- Developing a scalable and adaptable educational workflow.
- Focusing on practical application rather than solely theoretical knowledge.
Main Results:
- Existing literature defines AI knowledge but lacks practical application frameworks.
- The proposed workflow integrates AI learning into daily clinical teaching.
- The model is designed to be scalable and adaptable across institutions.
Conclusions:
- Medical educators must equip students with AI proficiency for safe clinical practice.
- A structured approach to AI education is crucial for responsible healthcare.
- The proposed framework facilitates effective learning and oversight of AI tools in medicine.
