Assessment of Differential Diagnoses for Oculoplastics Cases Produced by Large Language Models
LLMs show promising potential in diagnosing oculoplastic cases, with OcuSmart/EyeGPT and Claude 3.5 performing best for determining the case diagnosis and recall, and ChatGPT 3.5, OcuSmart/EyeGPT, and Gemini generating concise and relevant differentials. However, further research and development are necessary to validate LLMs' capabilities and integrate them into the clinical workflow.
Authors
- Sruti Rachapudi (ORCID: https://orcid.org/0000-0001-5097-9569)
- Thomas A. Oetting (ORCID: https://orcid.org/0000-0003-1764-610X)
- Sean M. Rodriguez (ORCID: https://orcid.org/0009-0003-9635-5665)
- Pete Setabutr
- Ann Q. Tran (ORCID: https://orcid.org/0000-0002-3176-5909)
- Jeffrey Peterson (ORCID: https://orcid.org/0000-0001-7059-1131)
- Kevin Heinze
- Sasha Hubschman
Institutions
- Cornell University (US)
- University of Illinois Chicago (US)
Publication Details
- Journal
- Ophthalmic Plastic and Reconstructive Surgery
- Published
- 2025-08-11
- DOI
- https://doi.org/10.1097/iop.0000000000002984
- Citations
- 1
- Primary Topic
- Biomedical Text Mining and Ontologies
- Type
- article
- Field-Weighted Citation Impact
- 0.81