Trial reportJournal of cataract and refractive surgery2026
Evaluating large language models vs residents in cataract and refractive surgery: comparative analysis using the American Academy of Ophthalmology Self-Assessment Program.
Trial report in Journal of cataract and refractive surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
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.
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Who cites it
3 citing papers in PubMed.
- GPT-5-assisted versus expert surgeon refractive planning in smooth incision lenticule keratomileusis (SILK): comparative analysis and visual outcomes.International ophthalmology · 2026Observational
- ChatGPT-4o and OpenAI-o1: A Comparative Analysis of Its Accuracy in Refractive Surgery.Journal of clinical medicine · 2025Article
- Advances in the application of artificial intelligence in ophthalmic education and clinical training.Frontiers in medicine · 2025Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo assess the accuracy of leading large language models (LLMs) in answering cataract and refractive surgery questions, determine whether prompt complexity affects performance, and compare their accuracy with ophthalmology residents.
settingNot applicable.
designRandomized questionnaire-based study using 100 questions from the cataract and refractive surgery section of the Basic and Clinical Science Course (BCSC) Self-Assessment Program.
methods5 LLMs (ChatGPT-4, ChatGPT-4o, Gemini, Gemini Advanced, and Copilot-Precise Mode) were tested. Data from 1983 BCSC Self-Assessment Program users served as a comparison. Each LLM underwent 2 sessions: one with a simple prompt and another with a contextualized prompt. Accuracy was defined as the proportion of correct answers.
resultsUsing the simple prompt, ChatGPT-4o achieved the highest accuracy at 84% (95% CI 77%-91%), followed by Gemini Advanced at 82%, Copilot at 78%, ChatGPT-4 at 77%, and Gemini at 62% ( P < .05). With the contextualized complex prompt, ChatGPT-4o again led (86%; 95% CI 79%-93%). Performance differences between simple and complex prompts were not statistically significant ( P > .05). Except for Gemini, all models' lower 95% CIs exceeded the 60% passing threshold. The mean resident score was 77% (95% CI 75%-79%). Only ChatGPT-4o significantly ( P = .04) outperformed residents, while Gemini Advanced and Copilot trended higher ( P ∼ .10).
conclusionsChatGPT-4o consistently outperformed ChatGPT-4, Gemini, Gemini Advanced, and Copilot, and was the only model to significantly surpass residents. Prompt complexity did not affect LLM performance. All models except Gemini exceeded the 60% accuracy threshold, indicating the potential of LLMs as tools for knowledge assessment in refractive surgery.
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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.