ArticleCureus2024
Performance of Artificial Intelligence Chatbots on Glaucoma Questions Adapted From Patient Brochures.
Article in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 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
- Comparison of the readability of ChatGPT and Bard in medical communication: a meta-analysis.BMC medical informatics and decision making · 2025Pooled it
- Psychological Risk Assessment in Plastic Surgery via a DeepSeek Large Language Model: A Retrospective Cohort Study.Aesthetic plastic surgery · 2026Article
- Applications of Large Language Models in Glaucoma: A Scoping Review.Vision (Basel, Switzerland) · 2026Review
- Evolving Consultation: Enhancing Ophthalmic Diagnostic Performance Using Large Language Model.Ophthalmology science · 2026Article
- Assessment of the Artificial Intelligence- Generated Fibromyalgia Information: Beyond the Hype.Archives of rheumatology · 2025Article
- ChatGPT-4 for addressing patient-centred frequently asked questions in age-related macular degeneration clinical practice.Eye (London, England) · 2025Article
- ChatGPT and Gemini for Patient Education: A Comparative Analysis of Common Pediatric Exanthematous Conditions.Cureus · 2025Article
- Breaking barriers: harnessing artificial intelligence for a stigma-free, efficient HIV prevention assessment among adults in South Africa.Frontiers in digital health · 2025Article
- Assessment of the information quality of chatbot technologies on orthodontic miniscrews.Dental press journal of orthodontics · 2025Article
- Opportunities and Challenges of Chatbots in Ophthalmology: A Narrative Review.Journal of personalized medicine · 2024Review
- Assessment of readability, reliability, and quality of ChatGPT®, BARD®, Gemini®, Copilot®, Perplexity® responses on palliative care.Medicine · 2024Observational
- Large language models in patient education: a scoping review of applications in medicine.Frontiers in medicine · 2024Article
- Quality and Readability of Patient Educational Materials Generated by ChatGPT-4o for Pediatric Ophthalmologic Surgeries.Journal of pediatric ophthalmology and strabismusArticle
- Evaluating Accuracy and Readability of Responses to Midlife Health Questions: A Comparative Analysis of Six Large Language Model Chatbots.Journal of mid-life healthArticle
- Article
- Evaluating large language models for glaucoma counseling: A pilot study of ChatGPT and Google Gemini responses in Traditional Chinese.Taiwan journal of ophthalmologyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction With the potential for artificial intelligence (AI) chatbots to serve as the primary source of glaucoma information to patients, it is essential to characterize the information that chatbots provide such that providers can tailor discussions, anticipate patient concerns, and identify misleading information. Therefore, the purpose of this study was to evaluate glaucoma information from AI chatbots, including ChatGPT-4, Bard, and Bing, by analyzing response accuracy, comprehensiveness, readability, word count, and character count in comparison to each other and glaucoma-related American Academy of Ophthalmology (AAO) patient materials. Methods Section headers from AAO glaucoma-related patient education brochures were adapted into question form and asked five times to each AI chatbot (ChatGPT-4, Bard, and Bing). Two sets of responses from each chatbot were used to evaluate the accuracy of AI chatbot responses and AAO brochure information, and the comprehensiveness of AI chatbot responses compared to the AAO brochure information, scored 1-5 by three independent glaucoma-trained ophthalmologists. Readability (assessed with Flesch-Kincaid Grade Level (FKGL), corresponding to the United States school grade levels), word count, and character count were determined for all chatbot responses and AAO brochure sections. Results Accuracy scores for AAO, ChatGPT, Bing, and Bard were 4.84, 4.26, 4.53, and 3.53, respectively. On direct comparison, AAO was more accurate than ChatGPT (p=0.002), and Bard was the least accurate (Bard versus AAO, p<0.001; Bard versus ChatGPT, p<0.002; Bard versus Bing, p=0.001). ChatGPT had the most comprehensive responses (ChatGPT versus Bing, p<0.001; ChatGPT versus Bard p=0.008), with comprehensiveness scores for ChatGPT, Bing, and Bard at 3.32, 2.16, and 2.79, respectively. AAO information and Bard responses were at the most accessible readability levels (AAO versus ChatGPT, AAO versus Bing, Bard versus ChatGPT, Bard versus Bing, all p<0.0001), with readability levels for AAO, ChatGPT, Bing, and Bard at 8.11, 13.01, 11.73, and 7.90, respectively. Bing responses had the lowest word and character count. Conclusion AI chatbot responses varied in accuracy, comprehensiveness, and readability. With accuracy scores and comprehensiveness below that of AAO brochures and elevated readability levels, AI chatbots require improvements to be a more useful supplementary source of glaucoma information for patients. Physicians must be aware of these limitations such that patients are asked about existing knowledge and questions and are then provided with clarifying and comprehensive information.
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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.