Large language models in ophthalmology: a review of publications from top ophthalmology journals
(2025)
Purpose
To review and evaluate the current literature on the application and impact of large language models (LLMs) in the field of ophthalmology, focusing on studies published in high-ranking ophthalmology journals.
Design
This is a retrospective review of published articles.
Participants
This study did not involve human participation.
Methods
Articles published in the first quartile (Q1) of ophthalmology journals on Scimago Journal & Country Rank discussing different LLMs up to June 7th, 2024, were reviewed, parsed, and analyzed.
Main Outcome Measures
All available articles were parsed and analyzed, which included the article and author characteristics and data regarding the LLM used and its applications, focusing on its use in medical education, clinical assistance, research, and patient education.
Results
There were 35 Q1-ranked journals identified, 19 of which contained articles discussing LLMs, with 101 articles eligible for review. One-third were original investigations (32%, 32/101), with an average of 5.3 authors per article. The United States (50.4%, 51/101) was the most represented country, followed by the United Kingdom (25.7%, 26/101) and Canada (16.8%, 17/101). ChatGPT was the most used LLM among the studies, with different versions discussed and compared. LLM applications were discussed relevant to their implications in medical education, clinical assistance, research, and patient education.
Conclusions
The numerous publications on the use of LLM in ophthalmology can provide valuable insights for stakeholders and consumers of these applications. LLMs present significant opportunities for advancement in ophthalmology, particularly in team science, education, clinical assistance, and research. While LLMs show promise, they also show challenges such as performance inconsistencies, bias, and ethical concerns. The study emphasizes the need for ongoing AI improvement, ethical guidelines, and multidisciplinary collaboration.
Purpose: To compare the assessment of clinically relevant retinal and choroidal lesions as well as optic nerve pathologies using a novel three-wavelength ultra-widefield (UWF) scanning laser ophthalmoscope with established retinal imaging techniques for ophthalmoscopic imaging.
Methods: Eighty eyes with a variety of retinal and choroidal lesions were assessed on the same time point using Topcon color fundus photography (CFP) montage, Optos red/green (RG), Heidelberg SPECTRALIS MultiColor 55-color montage (MCI), and novel Optos red/green/blue (RGB). Paired images of the optic nerve, retinal, or choroidal lesions were initially diagnosed based on CFP imaging. The accuracy of the imaging was then evaluated in comparison to CFP using a grading scale ranging from –1 (losing imaging information) to +1 (gaining imaging information).
Results: Eighty eyes of 43 patients with 116 retinal or choroidal pathologies, as well as 59 eyes with optic nerve imaging using CFP, MCI, RG, and RGB, were included in this study. Across all subgroups, RGB provided significantly more accurate clinical imaging with CFP as ground truth and compared to other modalities. This was true comparing RGB to both RG (P = 0.0225) and MCI (P < 0.001) overall. Although RGB provided more accurate clinical information overall, it was inferior to RG for melanocytic choroidal lesions (P = 0.011).
Conclusions: RGB can be considered as a useful tool to detect characteristics of central, midperipheral, and peripheral retinal lesions. Regarding melanocytic choroidal lesions, RGB was inferior to RG, and MCI was inferior to both RG and RGB modalities due to color changes.
Translational Relevance: Traditional retinal ultra-widefield imaging uses two wavelengths. Here, we evaluated three wavelengths for ultra-widefield imaging. We examined new optics (basic science) effect on patient imaging (clinical care).
