ArticleGraefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie2026
A large comprehensive comparison of large language models on ophthalmology board exams.
Article in Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeA comprehensive performance evaluation of seven state-of-the-art large language models (LLMs) compared to human resident benchmarks on ophthalmology board exam questions. Furthermore, this study aims to define current model strengths and limitations while validating an assessment tool for clinicians to evaluate model-generated outputs.
methodsSeven LLMs were assessed: ChatGPT-5, ChatGPT-4, Gemini 2.5 Pro, Gemini 2.5 Flash, Claude Sonnet 4.5, Grok-4-Fast-Reasoning, and Perplexity Sonar Pro. A dataset of 1,037 Israeli ophthalmology board questions (2020-2025) was manually categorized by question type (logical vs. informative), image modality, and 12 subspecialties. Models were evaluated for accuracy compared with resident performance, response latency, question difficulty, and self-assessed confidence.
resultsPerformance varied substantially across models. Gemini 2.5 Pro achieved the highest accuracy, followed by ChatGPT-5, both outperforming residents. Accuracy declined significantly with increasing question difficulty (Very Hard vs. Easy: aOR 0.28, 95% CI,0.16-0.46; aP < 0.001), mirroring resident trends. Questions containing images were significantly more challenging for all models. Logical reasoning questions were significantly more challenging for LLMs than informative ones (aOR 0.66, aP = 0.019). Confidence calibration varied widely, with ChatGPT-5 showing superior discrimination (AUC = 0.827).
conclusionsLLMs' performance on ophthalmology board questions varied across models. Gemini 2.5 Pro achieved the highest performance, surpassing the mean resident score. However, the majority of LLMs exhibit significant error rates and poor calibration of self-assessed confidence. To safely integrate LLMs into the clinical workflow, continuous evaluation is essential to guide ophthalmologists on LLMs' characteristics and clinical limitations.
Indexed as
Identifiers
42132999What 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.