ArticleEye (London, England)2025
Benchmarking the performance of large language models in uveitis: a comparative analysis of ChatGPT-3.5, ChatGPT-4.0, Google Gemini, and Anthropic Claude3.
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 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Comparison of the readability of ChatGPT and Bard in medical communication: a meta-analysis.BMC medical informatics and decision making · 2025Pooled it
- Patient and physician perspectives on large language model generated responses about brain aneurysm.Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery · 2026Article
- Automated Approaches of Text Simplification of Patient Education Materials: Scoping Review.Journal of medical Internet research · 2026Article
- Comparison of large language models for clinical scenario generation in medical education: a mixed-methods study.BMC medical education · 2026Article
- Comparative performance of GPT-4, GPT-o3, GPT-5, Gemini-3-Flash, and DeepSeek-R1 in ophthalmology question answering.Frontiers in cell and developmental biology · 2026Article
- Comparative evaluation of the quality, reliability, and readability of five large language model responses to frequently asked questions on gestational hypertension.Frontiers in public health · 2026Article
- Large language models in food and nutrition science: Opportunities, challenges, and the case of FoodyLLM.Current research in food science · 2026Article
- Evaluating the diagnostic reasoning of large language models in complex neuro-ophthalmological cases: a comparative analysis of GPT-o1 Pro, GPT-4o, Gemini, Grok 2 and DeepSeek.BMJ open ophthalmology · 2025Article
- Evaluating performance of large language models for atrial fibrillation management using different prompting strategies and languages.Scientific reports · 2025Article
- Reply to 'Comment on: Benchmarking the performance of large language models in uveitis: a comparative analysis of ChatGPT-3.5, ChatGPT-4.0, Google Gemini, and Anthropic Claude3'.Eye (London, England) · 2025Article
- Comment on: "Benchmarking the performance of large language models in uveitis: a comparative analysis of ChatGPT-3.5, ChatGPT-4.0, Google Gemini, and Anthropic Claude3".Eye (London, England) · 2025Article
- Large Language Models' Responses to Spinal Cord Injury: A Comparative Study of Performance.Journal of medical systems · 2025Article
- Large language models in the management of chronic ocular diseases: a scoping review.Frontiers in cell and developmental biology · 2025Review
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11 authors.
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No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND/
objectiveThis study aimed to evaluate the accuracy, comprehensiveness, and readability of responses generated by various Large Language Models (LLMs) (ChatGPT-3.5, Gemini, Claude 3, and GPT-4.0) in the clinical context of uveitis, utilizing a meticulous grading methodology.
methodsTwenty-seven clinical uveitis questions were presented individually to four Large Language Models (LLMs): ChatGPT (versions GPT-3.5 and GPT-4.0), Google Gemini, and Claude. Three experienced uveitis specialists independently assessed the responses for accuracy using a three-point scale across three rounds with a 48-hour wash-out interval. The final accuracy rating for each LLM response ('Excellent', 'Marginal', or 'Deficient') was determined through a majority consensus approach. Comprehensiveness was evaluated using a three-point scale for responses rated 'Excellent' in the final accuracy assessment. Readability was determined using the Flesch-Kincaid Grade Level formula. Statistical analyses were conducted to discern significant differences among LLMs, employing a significance threshold of p < 0.05.
resultsClaude 3 and ChatGPT 4 demonstrated significantly higher accuracy compared to Gemini (p < 0.001). Claude 3 also showed the highest proportion of 'Excellent' ratings (96.3%), followed by ChatGPT 4 (88.9%). ChatGPT 3.5, Claude 3, and ChatGPT 4 had no responses rated as 'Deficient', unlike Gemini (14.8%) (p = 0.014). ChatGPT 4 exhibited greater comprehensiveness compared to Gemini (p = 0.008), and Claude 3 showed higher comprehensiveness compared to Gemini (p = 0.042). Gemini showed significantly better readability compared to ChatGPT 3.5, Claude 3, and ChatGPT 4 (p < 0.001). Gemini also had fewer words, letter characters, and sentences compared to ChatGPT 3.5 and Claude 3.
conclusionsOur study highlights the outstanding performance of Claude 3 and ChatGPT 4 in providing precise and thorough information regarding uveitis, surpassing Gemini. ChatGPT 4 and Claude 3 emerge as pivotal tools in improving patient understanding and involvement in their uveitis healthcare journey.
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