AI in Medical Education: Opportunities, challenges, and the road ahead-Session Recording: AI in Medical Education

Video Transcription

Video Summary

This session explored the rapidly expanding role of artificial intelligence in medical education, especially in endocrinology training. Moderated by Dr. Julie Chen, the panel included Drs. Ruchi Gawba, Andrew Crawford, and Alistair Thompson, who shared perspectives on AI’s promise and risks.<br /><br />Panelists agreed AI can reduce administrative burden, personalize learning, improve curriculum design, and support literature review, feedback, and board preparation. However, they emphasized that AI must be used thoughtfully because of concerns about accuracy, hallucinated references, privacy, bias, and the risk of “de-skilling” or “mis-skilling” trainees who rely on it too heavily.<br /><br />A major theme was the need for clear AI policies in training programs. Dr. Gawba outlined four key principles: transparency, accountability, data privacy/security, and educational integrity. The panel favored a layered approach to policy development involving institutions, GME leadership, professional societies, and accrediting bodies.<br /><br />Live demonstrations showed practical uses of AI tools like Gemini and NotebookLM for building curricula and journal club materials. The panel stressed that AI should support, not replace, critical thinking and learner engagement. The session closed with discussion of future use in board review and undergraduate medical education.

Meta Tag

Concept Artificial Intelligence

Concept Medical Education

Concept AI Adoption

Concept Transparency

Concept Accountability

Keywords

artificial intelligence

medical education

endocrinology training

graduate medical education

AI ethics

faculty training

clinical accuracy

educational technology

AI policy

data privacy

curriculum design

board preparation

critical thinking

Artificial Intelligence

Medical Education

AI Adoption

Transparency

Accountability


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