Comparing Performance of Large Language Model-Based Tools on Patient-Driven Glaucoma Inquiries
GPT models surpass Gemini Pro in addressing commonly asked questions about glaucoma, providing valuable insights into the application of LLMs for providing health information.
Authors
- Enchi K. Chang (ORCID: https://orcid.org/0000-0002-6726-4508)
- Sarah Wagner (ORCID: https://orcid.org/0000-0002-0223-2388)
- Marc D. Succi (ORCID: https://orcid.org/0000-0002-1518-3984)
- Edward S. Lu (ORCID: https://orcid.org/0000-0002-1281-156X)
- Arya Rao (ORCID: https://orcid.org/0000-0003-3007-4812)
- Andrew W. Gross (ORCID: https://orcid.org/0000-0001-6605-670X)
- Dhruva Gupta (ORCID: https://orcid.org/0000-0002-3391-9908)
- Alexandra G. Castillejos Ellenthal
Institutions
- Massachusetts Eye and Ear Infirmary (US)
- Harvard University (US)
- Massachusetts General Hospital (US)
- Health Care Foundation (CA)
- Mass General Brigham (US)
Publication Details
- Journal
- Journal of Glaucoma
- Published
- 2025-09-09
- DOI
- https://doi.org/10.1097/ijg.0000000000002627
- Primary Topic
- Acute Ischemic Stroke Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00