ArticleInternational ophthalmology2023
A comparative study on the knowledge levels of artificial intelligence programs in diagnosing ophthalmic pathologies and intraocular tumors evaluated their superiority and potential utility.
Article in International ophthalmology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.
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.
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Who cites it
9 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Evaluating Large Language Models in Ophthalmology: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Evaluating the Performance of ChatGPT on Board-Style Examination Questions in Ophthalmology: A Meta-Analysis.Journal of medical systems · 2025Pooled it
- Ability of Large Language Models to Answer Patients' Questions and Generate Educational Materials for Uncommon Retinal Conditions.Journal of vitreoretinal diseases · 2026Article
- Review
- Development and evaluation of large-language models (LLMs) for oncology: A scoping review.PLOS digital health · 2025Article
- Review
- Applications of ChatGPT in the diagnosis, management, education, and research of retinal diseases: a scoping review.International journal of retina and vitreous · 2024Article
- Disparities in medical recommendations from AI-based chatbots across different countries/regions.Scientific reports · 2024Article
- Revolutionizing ocular cancer management: a narrative review on exploring the potential role of ChatGPT.Frontiers in public health · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThis study aimed to test the knowledge levels of ChatGPT, Bing, and Bard artificial intelligence chatbots, which have been released by three different manufacturers, about ophthalmic pathologies and intraocular tumors, to test their usability and to investigate the presence of superiority to each other.
methodsThirty-six questions were obtained from the American Academy and Ophthalmology 2022-2023 Basic and Clinical Science Course Ophthalmic Pathology and Intraocular Tumor study questions section. Each question was asked separately for the ChatGPT, Bing, and Bard artificial intelligence programs. Answers to the questions were categorized as correct or incorrect. The statistical relationship between the correct and incorrect response rates of the artificial intelligence programs was determined.
resultsFrom the artificial intelligence chatbots, ChatGPT gave the correct answer to 58.6% of the questions asked, Bing gave the correct answer to 63.9%, and Bard gave the correct answer to 69.4%. No statistical significance was found between the rates of correct answers to the questions in all 3 artificial intelligence programs (p = 0.705, Pearson Chi-square test).
conclusionArtificial intelligence chatbots can be used to access information related to ophthalmic pathologies and intraocular tumors. However, in the evaluation of the data, it should be noted that not all questions can be answered correctly. Care should be taken when examining the answers.
Indexed as
Identifiers
37880412What 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.