ArticleInternational journal of retina and vitreous2023
"Application and accuracy of artificial intelligence-derived large language models in patients with age related macular degeneration".
Article in International journal of retina and vitreous, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Evaluating Large Language Models in Ophthalmology: Systematic Review.Journal of medical Internet research · 2025Pooled it
- The Role of ChatGPT in Reducing Preoperative Anxiety in Patients Planning to Undergo Rhinoplasty.Aesthetic plastic surgery · 2026Trial
- Evaluating large language models vs residents in cataract and refractive surgery: comparative analysis using the American Academy of Ophthalmology Self-Assessment Program.Journal of cataract and refractive surgery · 2026Trial
- Language-assisted multimodal convolutional transformer pipeline for retinal lesions segmentation.Scientific reports · 2026Article
- Large Language Models in Ophthalmology: A Bibliographic Analysis.Turkish journal of ophthalmology · 2026Article
- Evolving Consultation: Enhancing Ophthalmic Diagnostic Performance Using Large Language Model.Ophthalmology science · 2026Article
- Language assisted learnable hyperdimensional computing framework for retinal disease classification.Scientific reports · 2026Article
- Can Multimodal Large Language Models Diagnose Diabetic Retinopathy from Fundus Photos? A Quantitative Evaluation.Ophthalmology science · 2026Article
- Global perspectives of ophthalmologists on artificial intelligence adoption in clinical practice.International journal of retina and vitreous · 2025Article
- Large language models in ophthalmology: a scoping review on their utility for clinicians, researchers, patients, and educators.Eye (London, England) · 2025Article
- ChatGPT-4o and OpenAI-o1: A Comparative Analysis of Its Accuracy in Refractive Surgery.Journal of clinical medicine · 2025Article
- ChatGPT-4 for addressing patient-centred frequently asked questions in age-related macular degeneration clinical practice.Eye (London, England) · 2025Article
- Evolutionary patterns and research frontiers of artificial intelligence in age-related macular degeneration: a bibliometric analysis.Quantitative imaging in medicine and surgery · 2025Article
- Comparison of the Accuracy, Comprehensiveness, and Readability of ChatGPT, Google Gemini, and Microsoft Copilot on Dry Eye Disease.Beyoglu eye journal · 2025Article
- Opportunities and Challenges of Chatbots in Ophthalmology: A Narrative Review.Journal of personalized medicine · 2024Review
- Prospective validation of a virtual post-operative clinic in vitreoretinal surgery.Eye (London, England) · 2024Article
- Assessing large language models' accuracy in providing patient support for choroidal melanoma.Eye (London, England) · 2024Article
- Applications of ChatGPT in the diagnosis, management, education, and research of retinal diseases: a scoping review.International journal of retina and vitreous · 2024Article
- The digital age in retinal practice.International journal of retina and vitreous · 2024Article
- Artificial intelligence derived large language model in decision-making process in uveitis.International journal of retina and vitreous · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionAge-related macular degeneration (AMD) affects millions of people globally, leading to a surge in online research of putative diagnoses, causing potential misinformation and anxiety in patients and their parents. This study explores the efficacy of artificial intelligence-derived large language models (LLMs) like in addressing AMD patients' questions.
methodsChatGPT 3.5 (2023), Bing AI (2023), and Google Bard (2023) were adopted as LLMs. Patients' questions were subdivided in two question categories, (a) general medical advice and (b) pre- and post-intravitreal injection advice and classified as (1) accurate and sufficient (2) partially accurate but sufficient and (3) inaccurate and not sufficient. Non-parametric test has been done to compare the means between the 3 LLMs scores and also an analysis of variance and reliability tests were performed among the 3 groups.
resultsIn category a) of questions, the average score was 1.20 (± 0.41) with ChatGPT 3.5, 1.60 (± 0.63) with Bing AI and 1.60 (± 0.73) with Google Bard, showing no significant differences among the 3 groups (p = 0.129). The average score in category b was 1.07 (± 0.27) with ChatGPT 3.5, 1.69 (± 0.63) with Bing AI and 1.38 (± 0.63) with Google Bard, showing a significant difference among the 3 groups (p = 0.0042). Reliability statistics showed Chronbach's α of 0.237 (range 0.448, 0.096-0.544).
conclusionChatGPT 3.5 consistently offered the most accurate and satisfactory responses, particularly with technical queries. While LLMs displayed promise in providing precise information about AMD; however, further improvements are needed especially in more technical questions.
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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.