ArticleBeyoglu eye journal2025
Comparison of the Accuracy, Comprehensiveness, and Readability of ChatGPT, Google Gemini, and Microsoft Copilot on Dry Eye Disease.
Article in Beyoglu eye journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Accuracy, readability, and content coverage of AI-generated responses to questions on functional appliances.BMC oral health · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: This study compared the performance of ChatGPT, Google Gemini, and Microsoft Copilot in answering 25 questions about dry eye disease and evaluated comprehensiveness, accuracy, and readability metrics. Methods: The artificial intelligence (AI) platforms answered 25 questions derived from the American Academy of Ophthalmology's Eye Health webpage. Three reviewers assigned comprehensiveness (0-5) and accuracy (-2 to 2) scores. Readability metrics included Flesch-Kincaid Grade Level, Flesch Reading Ease Score, sentence/word statistics, and total content measures. Responses were rated by three independent reviewers. Readability metrics were also calculated, and platforms were compared using Kruskal-Wallis and Friedman tests with Results: Google Gemini demonstrated the highest comprehensiveness and accuracy scores, significantly outperforming Microsoft Copilot (p<0.001). ChatGPT produced the most sentences and words (p<0.001), while readability metrics showed no significant differences among models (p>0.05). Inter-observer reliability was highest for Google Gemini (ICC=0.701), followed by ChatGPT (ICC=0.578), with Microsoft Copilot showing the lowest agreement (ICC=0.495). These results indicate Google Gemini's superior performance and consistency, whereas Microsoft Copilot had the weakest overall performance. Conclusion: Google Gemini excelled in content volume while maintaining high comprehensiveness and accuracy, outperforming ChatGPT and Microsoft Copilot in content generation. The platforms displayed comparable readability and linguistic complexity. These findings inform AI tool selection in health-related contexts, emphasizing Google Gemini's strengths in detailed responses. Its superior performance suggests potential utility in clinical and patient-facing applications requiring accurate and comprehensive content.
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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.