ArticleEye (London, England)2025
ChatGPT-4 for addressing patient-centred frequently asked questions in age-related macular degeneration clinical practice.
Article in Eye (London, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Ability of Large Language Models to Answer Patients' Questions and Generate Educational Materials for Uncommon Retinal Conditions.Journal of vitreoretinal diseases · 2026Article
- Mapping the Reliability-Readability Gap in the Education of Patients With Age-Related Macular Degeneration Across 6 Large Language Models: Comparative Evaluation Study.JMIR medical informatics · 2026Article
- Report Generation System for Slit-Lamp Image Interpretation Using Vision-Language Models.Ophthalmology and therapy · 2026Article
- Automated Report Generation in Ophthalmology: Integrating Artificial Intelligence, Multimodal Imaging, and Clinical Data.Ophthalmology and therapy · 2026Review
- Evolving Consultation: Enhancing Ophthalmic Diagnostic Performance Using Large Language Model.Ophthalmology science · 2026Article
- Advances in the application of artificial intelligence in ophthalmic education and clinical training.Frontiers in medicine · 2025Review
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
purposeLarge language models have shown promise in answering questions related to medical conditions. This study evaluated the responses of ChatGPT-4 in answering patient-centred frequently asked questions (FAQs) relevant in age-related macular degeneration (AMD).
methodsTen experts across a range of clinical, education and research practices in optometry and ophthalmology. Over 200 patient-centric FAQs from authoritative professional society, hospital and advocacy websites were condensed into 37 questions across four themes: definition, causes and risk factors, symptoms and detection, and treatment and follow-up. The questions were individually input into ChatGPT-4 to generate responses. The responses were graded by the experts individually using a 5-point Likert scale (1 = strongly disagree; 5 = strongly agree) across four domains: coherency, factuality, comprehensiveness, and safety.
resultsAcross all themes and domains, median scores were all 4 ("agree"). Comprehensiveness had the lowest scores across domains (mean 3.8 ± 0.8), followed by factuality (mean 3.9 ± 0.8), safety (mean 4.1 ± 0.8) and coherency (mean 4.3 ± 0.7). Examination of the individual 37 questions showed that 5 (14%), 21 (57%), 23 (62%) and 9 (24%) of the questions had average scores below 4 (below "agree") for the coherency, factuality, comprehensiveness and safety domains, respectively. Free-text comments highlighted issues related to superseded or older technologies, and techniques that are not routinely used in clinical practice, such as genetic testing.
conclusionsChatGPT-4 responses to FAQs in AMD were generally agreeable in terms of coherency, factuality, comprehensiveness, and safety. However, areas of weakness were identified, precluding recommendations for routine use to provide patients with tailored counselling in AMD.
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Registered trials
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