ArticleEye & contact lens2025
Evaluation of Responses to Questions About Keratoconus Using ChatGPT-4.0, Google Gemini and Microsoft Copilot: A Comparative Study of Large Language Models on Keratoconus.
Article in Eye & contact lens, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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.
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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
9 citing papers in PubMed.
- Accuracy and Readability of Chat Generative Pre-Trained Transformer-4 Omni in Answering Ophthalmology Patient Questions.Ophthalmology science · 2026Article
- Large Language model (LLM) in temporomandibular disorder education: a comparative study.BMC oral health · 2025Article
- What artificial intelligence (AI) can tell us about Nasoalveolar Molding (NAM)?BMC oral health · 2025Article
- Accuracy of ChatGPT, Gemini, Copilot, and Claude to Blepharoplasty-Related Questions.Aesthetic plastic surgery · 2025Article
- How Accurate Is AI? A Critical Evaluation of Commonly Used Large Language Models in Responding to Patient Concerns About Incidental Kidney Tumors.Journal of clinical medicine · 2025Article
- ChatGPT and Microsoft Copilot for Cochlear Implant Side Selection: A Preliminary Study.Audiology research · 2025Article
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- Large language models in the management of chronic ocular diseases: a scoping review.Frontiers in cell and developmental biology · 2025Review
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Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesLarge language models (LLMs) are increasingly being used today and are becoming increasingly important for providing accurate clinical information to patients and physicians. This study aimed to evaluate the effectiveness of generative pre-trained transforme-4.0 (ChatGPT-4.0), Google Gemini, and Microsoft Copilot LLMs in responding to patient questions regarding keratoconus.
methodsThe LLMs' responses to the 25 most common questions about keratoconus asked by real-life patients were blindly rated by two ophthalmologists using a 5-point Likert scale. In addition, the DISCERN scale was used to evaluate the responses of the language models in terms of reliability, and the Flesch reading ease and Flesch-Kincaid grade level indices were used to determine readability.
resultsChatGPT-4.0 provided more detailed and accurate answers to patients' questions about keratoconus than Google Gemini and Microsoft Copilot, with 92% of the answers belonging to the "agree" or "strongly agree" categories. Significant differences were observed between all three LLMs on the Likert scale ( P <0.001).
conclusionsAlthough the answers of ChatGPT-4.0 to questions about keratoconus were more complex for patients than those of other language programs, the information provided was reliable and accurate.
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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 Socratic graph.