ArticleThe British journal of ophthalmology2024
Comparing generative and retrieval-based chatbots in answering patient questions regarding age-related macular degeneration and diabetic retinopathy.
Article in The British journal of ophthalmology, 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.
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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
- Accuracy of Large Language Models When Answering Clinical Research Questions: Systematic Review and Network Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Evolving Consultation: Enhancing Ophthalmic Diagnostic Performance Using Large Language Model.Ophthalmology science · 2026Article
- Can Multimodal Large Language Models Diagnose Diabetic Retinopathy from Fundus Photos? A Quantitative Evaluation.Ophthalmology science · 2026Article
- Bilingual evaluation of large language models for patient education in refractive surgery.International journal of ophthalmology · 2026Article
- [Application of large language models in health education for patients with pediatric cataract].Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2025Article
- Evaluating the clinical utility of multimodal large language models for detecting age-related macular degeneration from retinal imaging.Scientific reports · 2025Article
- Ophthalmological Question Answering and Reasoning Using OpenAI o1 vs Other Large Language Models.JAMA ophthalmology · 2025Article
- ChatGPT-4 for addressing patient-centred frequently asked questions in age-related macular degeneration clinical practice.Eye (London, England) · 2025Article
- Article
- Comparing Ophthalmologist and Artificial Intelligence Chatbot Responses to Patient Questions.Clinical ophthalmology (Auckland, N.Z.) · 2025Article
- Large language models in the management of chronic ocular diseases: a scoping review.Frontiers in cell and developmental biology · 2025Review
- Applications of ChatGPT in the diagnosis, management, education, and research of retinal diseases: a scoping review.International journal of retina and vitreous · 2024Article
- Evaluating the performance of ChatGPT-3.5 and ChatGPT-4 on the Taiwan plastic surgery board examination.Heliyon · 2024Article
- Large language models in patient education: a scoping review of applications in medicine.Frontiers in medicine · 2024Article
- Can OpenAI's New o1 Model Outperform Its Predecessors in Common Eye Care Queries?Ophthalmology scienceArticle
- Large Language Models in Ophthalmology: A Review of Publications from Top Ophthalmology Journals.Ophthalmology scienceArticle
Corrections and comments
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Authors and funding
16 authors.
Funding
Abstract
BACKGROUND/
aimsTo compare the performance of generative versus retrieval-based chatbots in answering patient inquiries regarding age-related macular degeneration (AMD) and diabetic retinopathy (DR).
methodsWe evaluated four chatbots: generative models (ChatGPT-4, ChatGPT-3.5 and Google Bard) and a retrieval-based model (OcularBERT) in a cross-sectional study. Their response accuracy to 45 questions (15 AMD, 15 DR and 15 others) was evaluated and compared. Three masked retinal specialists graded the responses using a three-point Likert scale: either 2 (good, error-free), 1 (borderline) or 0 (poor with significant inaccuracies). The scores were aggregated, ranging from 0 to 6. Based on majority consensus among the graders, the responses were also classified as 'Good', 'Borderline' or 'Poor' quality.
resultsOverall, ChatGPT-4 and ChatGPT-3.5 outperformed the other chatbots, both achieving median scores (IQR) of 6 (1), compared with 4.5 (2) in Google Bard, and 2 (1) in OcularBERT (all p ≤8.4×10
conclusionChatGPT-4 and ChatGPT-3.5 demonstrated superior performance, followed by Google Bard and OcularBERT. Generative chatbots are potentially capable of answering domain-specific questions outside their original training. Further validation studies are still required prior to real-world implementation.
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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.