EVALUATING LARGE LANGUAGE MODELS ON THEIR ACCURACY AND COMPLETENESS
All three LLMs provided highly accurate and complete responses to questions centered on immune checkpoint inhibitor ocular toxicities and management. Further studies are needed to assess specific immune checkpoint inhibitor agents and the accuracy and completeness of updated versions of LLMs.
Authors
- Jasmine H. Francis (ORCID: https://orcid.org/0000-0002-7637-3108)
- Rayna Marshall (ORCID: https://orcid.org/0000-0002-6961-1278)
- Lauren A. Dalvin (ORCID: https://orcid.org/0000-0001-9710-4284)
- Meghan Berkenstock (ORCID: https://orcid.org/0000-0001-7517-7706)
- Kapil Mishra
- Hannah Xu
- Camellia Edalat
- Nila Kirupaharan
Institutions
- Mayo Clinic (US)
- Memorial Sloan Kettering Cancer Center (US)
- Johns Hopkins University (US)
- University of California, Irvine (US)
- Johns Hopkins Medicine (US)
- University of California San Diego (US)
- Mayo Clinic in Arizona (US)
- The California Eye Institute (US)
- Drexel University (US)
Publication Details
- Journal
- Retina
- Published
- 2024-09-23
- DOI
- https://doi.org/10.1097/iae.0000000000004271
- Citations
- 3
- Primary Topic
- Peptidase Inhibition and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.85