Using Large Language Model Artificial Intelligence to Enhance Clinical Competency Committee Insight
Artificial intelligence can generate EM resident assessments with comparable or superior ratings of quality, accuracy, and specificity relative to human-generated assessments. This highlights the potential of AI-driven evaluations to streamline educator review processes, reducing workload without sacrificing integrity.
Authors
- Mirinda Ann Gormley (ORCID: https://orcid.org/0000-0002-8158-7055)
- Camiron L. Pfennig (ORCID: https://orcid.org/0000-0002-6622-1899)
- Carl Ehrett (ORCID: https://orcid.org/0000-0002-0711-0347)
- Mark Pittman
- M. Cifuni
- Ray Chahoud
Institutions
- Prisma Health (US)
- University of South Carolina (US)
- Clemson University (US)
Publication Details
- Journal
- AEM Education and Training
- Published
- 2026-02-01
- DOI
- https://doi.org/10.1002/aet2.70142
- Primary Topic
- Innovations in Medical Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00