ArticleOphthalmology science2026
Evaluator-Masked Assessment of 4 Consumer-Facing Large Language Models on Expert Anterior Segment Ophthalmology Questions.
Article in Ophthalmology science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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.
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Objective: To compare expert-rated quality and safety of responses from four contemporary large language models (LLMs)-ChatGPT, Gemini, Claude, and Grok-on expert-level anterior segment ophthalmology questions. Design: Cross-sectional, masked, randomized-display-order, multievaluator comparative study. Participants: Eleven fellowship-trained cornea and anterior segment subspecialists. Methods: Forty-five expert-level questions spanning six anterior segment domains were administered to each LLM under zero-shot conditions in a single prespecified session (January 19, 2026), with memory, browsing, and retrieval disabled. Responses were masked to model identity and independently rated on five 5-point Likert metrics (expert-rated accuracy, completeness/clinical utility, clarity/readability, overall safety, and potential for harm). No specialist-authored reference answer key was used; all metrics index expert-rated perception rather than verified clinical correctness. Main Outcome Measures: Median [interquartile range], mean ± standard deviation, favorable-rating proportion (≤2) with 95% bootstrap confidence interval, Friedman omnibus with Kendall's W, and Bonferroni-adjusted pairwise Wilcoxon tests with rank-biserial correlation. Prespecified sensitivity analyses included length-adjusted mixed models and a masking-integrity diagnostic; post hoc analyses added question-level severe-safety and style-restricted comparisons. Results: All 9900 ratings (45 questions × 4 LLMs × 5 metrics × 11 evaluators) were complete. Differences were significant for all three quality metrics (Friedman Conclusions: Claude and Grok received more favorable expert-rated quality than ChatGPT and Gemini. Post hoc, a larger severe-safety-rating tail was identified for these two models despite no significant difference in mean safety; this signal is hypothesis-generating. The findings reflect expert preference, not validated clinical correctness, at one time point. Financial Disclosures: The authors have no proprietary or commercial interest in any materials discussed in this article.
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