ArticleEye (London, England)2025
Large language models in ophthalmology: a scoping review on their utility for clinicians, researchers, patients, and educators.
Article in Eye (London, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Accuracy and Effectiveness of AI-Powered Systems in Patient Counseling, Education, and Management in Optometry and Related Eye-Care Settings: A Systematic Review.Healthcare (Basel, Switzerland) · 2026Review
- How Far Have Large Language Models Advanced in Ophthalmology? A Systematic Review of Their Development, Evaluation, and Readiness for Clinical Use.Research square · 2026Article
- Accuracy, readability, and bias of GPT-4o mini responses to oculoplastic patient questions.Frontiers in ophthalmology · 2026Article
- Evaluating multimodal large language models for differential diagnosis of high myopia versus high myopia with Glaucoma.Frontiers in medicine · 2026Article
- Benchmark evaluation of multi-modal large language models for ophthalmic diagnosis in real world.Frontiers in medicine · 2026Article
- Review
- Advances in the application of artificial intelligence in ophthalmic education and clinical training.Frontiers in medicine · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.