Evaluating the Agreement Between ChatGPT and the Clinical Competency Committee in Assigning ACGME Milestones for Family Medicine Residents
This study demonstrates the feasibility for tools like ChatGPT to assist in the evaluation process of family medicine residents without apparent bias based on gender or word count.
Authors
- Karl T Clebak (ORCID: https://orcid.org/0000-0002-0764-5659)
- Michael Partin (ORCID: https://orcid.org/0000-0002-7496-6604)
- Anthony B. Dambro (ORCID: https://orcid.org/0000-0003-0165-0403)
- Lan Kong (ORCID: https://orcid.org/0000-0001-6098-9445)
- Yiduo Shang (ORCID: https://orcid.org/0000-0003-3770-4143)
- Roland Newman
Institutions
- Pennsylvania State University (US)
- Penn State Milton S. Hershey Medical Center (US)
Publication Details
- Journal
- Family Medicine
- Published
- 2025-06-06
- DOI
- https://doi.org/10.22454/fammed.2025.363712
- Citations
- 1
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.65