ArticleEye (London, England)2024
ChatGPT-3.5 and Bing Chat in ophthalmology: an updated evaluation of performance, readability, and informative sources.
Article in Eye (London, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 3 of them syntheses that pooled it.
What it found
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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
17 citing papers in PubMed, 3 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
- Accuracy of Large Language Models When Answering Clinical Research Questions: Systematic Review and Network Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- A large language model for complex cardiology care.Nature medicine · 2026Trial
- Evolving Minds: Natural Learning vs. Artificial Learning in Ophthalmology Training.Turkish journal of ophthalmology · 2026Observational
- Enhancing the Readability of Online Pediatric Cataract Education Materials: A Comparative Study of Large Language Models.Translational vision science & technology · 2025Article
- Comparative analysis of language models in addressing syphilis-related queries.Medicina oral, patologia oral y cirugia bucal · 2025Article
- Utilizing ChatGPT-3.5 to Assist Ophthalmologists in Clinical Decision-making.Journal of ophthalmic & vision research · 2025Article
- Opportunities and Challenges of Chatbots in Ophthalmology: A Narrative Review.Journal of personalized medicine · 2024Review
- Qualitative metrics from the biomedical literature for evaluating large language models in clinical decision-making: a narrative review.BMC medical informatics and decision making · 2024Review
- Article
- Applications of ChatGPT in the diagnosis, management, education, and research of retinal diseases: a scoping review.International journal of retina and vitreous · 2024Article
- The potential of ChatGPT in medicine: an example analysis of nephrology specialty exams in Poland.Clinical kidney journal · 2024Article
- A comparative analysis of GPT-3.5 and GPT-4.0 on a multiple-choice ophthalmology question bank: A study on artificial intelligence developments.Romanian journal of ophthalmologyArticle
- Federated Learning in Glaucoma: A Comprehensive Review and Future Perspectives.Ophthalmology. GlaucomaReview
- Large Language Models in Ophthalmology: A Review of Publications from Top Ophthalmology Journals.Ophthalmology scienceArticle
- Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND/
objectivesExperimental investigation. Bing Chat (Microsoft) integration with ChatGPT-4 (OpenAI) integration has conferred the capability of accessing online data past 2021. We investigate its performance against ChatGPT-3.5 on a multiple-choice question ophthalmology exam. SUBJECTS/
methodsIn August 2023, ChatGPT-3.5 and Bing Chat were evaluated against 913 questions derived from the Academy's Basic and Clinical Science Collection collection. For each response, the sub-topic, performance, Simple Measure of Gobbledygook readability score (measuring years of required education to understand a given passage), and cited resources were collected. The primary outcomes were the comparative scores between models, and qualitatively, the resources referenced by Bing Chat. Secondary outcomes included performance stratified by response readability, question type (explicit or situational), and BCSC sub-topic.
resultsAcross 913 questions, ChatGPT-3.5 scored 59.69% [95% CI 56.45,62.94] while Bing Chat scored 73.60% [95% CI 70.69,76.52]. Both models performed significantly better in explicit than clinical reasoning questions. Both models performed best on general medicine questions than ophthalmology subsections. Bing Chat referenced 927 online entities and provided at-least one citation to 836 of the 913 questions. The use of more reliable (peer-reviewed) sources was associated with higher likelihood of correct response. The most-cited resources were eyewiki.aao.org, aao.org, wikipedia.org, and ncbi.nlm.nih.gov. Bing Chat showed significantly better readability than ChatGPT-3.5, averaging a reading level of grade 11.4 [95% CI 7.14, 15.7] versus 12.4 [95% CI 8.77, 16.1], respectively (p-value < 0.0001, ρ = 0.25).
conclusionsThe online access, improved readability, and citation feature of Bing Chat confers additional utility for ophthalmology learners. We recommend critical appraisal of cited sources during response interpretation.
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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.