OSCEai dermatology: augmenting dermatologic medical education with Large Language Model GPT-4
OSCEai Dermatology demonstrates how large language models (LLMs) like GPT-4 can be integrated into medical education to enhance trainees' history taking and management skills in an OSCE-like format, including in visual-based specialties like dermatology. By generating diverse, realistic skin cancer role-play scenarios across different skin tones alongside the integration of pre-existing, evidence-based images, the app provides learners with valuable, personalized feedback. This innovation offers a novel, interactive learning tool that supplements traditional teaching methods and can be applied across various specialties. Institutions can adopt or adapt similar LLM-driven educational tools to introduce trainees to a wider range of clinical cases, fostering improved diagnostic skills and patient-centred, culturally sensitive care.
Authors
- Abrahim Abduelmula (ORCID: https://orcid.org/0000-0001-9137-5876)
- Sara Mirali (ORCID: https://orcid.org/0000-0001-6233-9067)
- Trevor Champagne (ORCID: https://orcid.org/0000-0002-7743-5403)
- Nikki Nathanielsz
- Tatiana Lapa (ORCID: https://orcid.org/0000-0001-9937-4755)
- Muskaan Sachdeva (ORCID: https://orcid.org/0000-0002-2252-5663)
- Eddie Guo (ORCID: https://orcid.org/0000-0002-7223-0505)
- Ye‐Jean Park
- Brian D. Rankin (ORCID: https://orcid.org/0000-0001-9283-6312)
- Bryan Ma (ORCID: https://orcid.org/0000-0002-9637-5530)
- Purnima Gupta (ORCID: https://orcid.org/0000-0001-8063-3075)
Institutions
- University of Calgary (CA)
- University of Toronto (CA)
Publication Details
- Journal
- Canadian Medical Education Journal
- Published
- 2025-08-08
- DOI
- https://doi.org/10.36834/cmej.80056
- Citations
- 2
- Primary Topic
- Medicine and Dermatology Studies History
- Type
- article
- Field-Weighted Citation Impact
- 4.01