Can Artificial Intelligence Deceive Residency Committees? A Randomized Multicenter Analysis of Letters of Recommendation
Faculty members were unsuccessful in determining the difference between human-generated and AI-generated LORs 50% of the time, which suggests that AI can generate LORs similarly to human authors. This highlights the importance for selection committees to reconsider the role and influence of LORs on residency applications.
Authors
- Eric G. Huish (ORCID: https://orcid.org/0000-0002-6025-7786)
- Hai V Le (ORCID: https://orcid.org/0000-0002-9111-9060)
- Samuel K. Simister (ORCID: https://orcid.org/0000-0001-6441-7815)
- John P. Meehan (ORCID: https://orcid.org/0000-0002-3752-4719)
- Augustine M. Saiz (ORCID: https://orcid.org/0000-0002-1955-8667)
- Zachary C. Lum (ORCID: https://orcid.org/0000-0002-5871-8539)
- Eugene Y. Tsai
- Andrea Halim
- Dominick Tuason
Institutions
- San Joaquin General Hospital (US)
- Kleijnen Systematic Reviews (United Kingdom) (GB)
- Centre Alpien de Phytogéographie (CH)
Publication Details
- Journal
- Journal of the American Academy of Orthopaedic Surgeons
- Published
- 2024-12-12
- DOI
- https://doi.org/10.5435/jaaos-d-24-00438
- Citations
- 4
- Primary Topic
- Diversity and Career in Medicine
- Type
- article
- Field-Weighted Citation Impact
- 3.72