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Large language models in ophthalmology: a scoping review on their utility for clinicians, researchers, patients, and educators

Eye volume 39, pages 2752–2761 (2025)Cite this article

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Abstract

Since its introduction in November 2022, the public interest in the utility of large language models (LLMs) has gained widespread adoption among individual consumers and among medical practitioners, with a consequent increase in publications describing their utility in healthcare. This review highlights original research articles on how LLM’s can be utilized by various stakeholders in ophthalmology through clinical assistance, patient education, medical education, and research. ChatGPT consistently responds with better accuracy and quality than other LLMs across various studies employing different methodologies, with newer iterations offering more advantages. Studies have likewise identified limitations of LLMs, which include hallucination, inability to interpret image-based prompts, and limited performance across non-English languages. As newer iterations of available and more advanced models with image processing are currently being introduced, generative artificial intelligence should be continuously monitored for its implications in eye care.

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Funding

FGPK - National Institutes of Health Bridge2AI (AI-READI Salutogenesis Grand Challenge) Grant OT2OD032644.

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Author notes

  1. These authors contributed equally: Jose Carlo M. Artiaga, Ma. Carmela B. Guevarra, George Michael N. Sosuan.

Authors and Affiliations

  1. Department of Ophthalmology and Visual Sciences, Philippine General Hospital, University of the Philippines Manila, Manila City, Philippines

    Jose Carlo M. Artiaga & George Michael N. Sosuan

  2. Massachusetts Eye and Ear, Boston, MA, USA

    Ma. Carmela B. Guevarra

  3. Harvard Medical School, Department of Ophthalmology, Boston, MA, USA

    Ma. Carmela B. Guevarra

  4. Jacobs Retina Center, University of California, San Diego, CA, USA

    Akshay Prashant Agnihotri, Ines Doris Nagel & Fritz Gerald P. Kalaw

  5. Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California, San Diego, CA, USA

    Fritz Gerald P. Kalaw

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JCMA, MCBG, GMNS, FGPK - designed the study, acquired, parsed, and interpreted the data, drafted and revised the manuscript, and approved the final version of the manuscript. APA, IDN – designed the study, acquired the data, and approved the final version of the manuscript.

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Correspondence to Fritz Gerald P. Kalaw.

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Artiaga, J.C.M., Guevarra, M.C.B., Sosuan, G.M.N. et al. Large language models in ophthalmology: a scoping review on their utility for clinicians, researchers, patients, and educators. Eye 39, 2752–2761 (2025). https://doi.org/10.1038/s41433-025-03935-7

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