ArticleOphthalmology science
OpenAI o1 Large Language Model Outperforms GPT-4o, Gemini 1.5 Flash, and Human Test Takers on Ophthalmology Board-Style Questions.
Article in Ophthalmology science. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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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
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Who cites it
9 citing papers in PubMed.
- Large Language Models Approximate Inter-Expert Agreement in Glaucoma Suspect and Glaucoma Classification from Multimodal Data.Ophthalmology science · 2026Article
- Comparing the performance of four mainstream large language models on medical literature review generation: a human expert evaluation in SMILE surgery.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Article
- Comparative Performance of Gemini 3 Pro and GPT-5 Family Models on Ophthalmology Board-Style Questions.Ophthalmology science · 2026Article
- Benchmarking LLM decision support in inflammatory aneurysms: DeepSeek-R1, DeepSeek-V3 and ChatGPT-4o.Clinical and experimental medicine · 2026Article
- Utility of ChatGPT-4o in Creating Patient Handouts in Ophthalmology: A Comparison With American Academy of Ophthalmology Educational Materials.Translational vision science & technology · 2026Article
- Large language models for ophthalmic examination understanding: from information extraction to clinical decision support.Frontiers in medicine · 2026Review
- When AI models take the exam: large language models vs medical students on multiple-choice course exams.Medical education online · 2025Article
- Advances in the application of artificial intelligence in ophthalmic education and clinical training.Frontiers in medicine · 2025Review
- Comparative Performance Evaluation of Large Language Models and Human Teachers in Answering Optometry Questions from Medical Undergraduates.Journal of medical education and curricular developmentArticle
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Purpose: To evaluate and compare the performance of human test takers and three artificial intelligence (AI) models-OpenAI o1, ChatGPT-4o, and Gemini 1.5 Flash-on ophthalmology board-style questions, focusing on overall accuracy and performance stratified by ophthalmic subspecialty and cognitive complexity level. Design: A cross-sectional study. Subjects: Five hundred questions sourced from the Methods: Three large language models interpreted the questions using standardized prompting procedures. Subanalysis was performed, stratifying the questions by subspecialty and complexity defined by the Buckwalter taxonomic schema. Statistical analysis, including the analysis of variance and McNemar test, was conducted to assess performance differences. Main Outcome Measures: Accuracy of responses for each model and human test takers, stratified by subspecialty and cognitive complexity. Results: OpenAI o1 achieved the highest overall accuracy (423/500, 84.6%), significantly outperforming GPT-4o (331/500, 66.2%; Conclusions: OpenAI o1 outperformed GPT-4o, Gemini, and human test takers in answering ophthalmology board-style questions from two question banks and across three complexity levels. These findings highlight advances in AI technology and OpenAI o1's growing potential as an adjunct in ophthalmic education and care. Financial Disclosures: The author(s) have no proprietary or commercial interest in any materials discussed in this article.
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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.