ArticleClinical ophthalmology (Auckland, N.Z.)2024
Comparing the Ability of Google and ChatGPT to Accurately Respond to Oculoplastics-Related Patient Questions and Generate Customized Oculoplastics Patient Education Materials.
Article in Clinical ophthalmology (Auckland, N.Z.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 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
- Ability of Large Language Models to Answer Patients' Questions and Generate Educational Materials for Uncommon Retinal Conditions.Journal of vitreoretinal diseases · 2026Article
- Utility of ChatGPT-4o in Creating Patient Handouts in Ophthalmology: A Comparison With American Academy of Ophthalmology Educational Materials.Translational vision science & technology · 2026Article
- Accuracy and Readability of Chat Generative Pre-Trained Transformer-4 Omni in Answering Ophthalmology Patient Questions.Ophthalmology science · 2026Article
- Accuracy, readability, and bias of GPT-4o mini responses to oculoplastic patient questions.Frontiers in ophthalmology · 2026Article
- Potential of AI Chatbots in Online Hair Transplantation Consultations: A Multi-metric Assessment of Three Models.Aesthetic plastic surgery · 2025Article
- Assessing the Capability of Large Language Models for Navigation of the Australian Health Care System: Comparative Study.JMIR AI · 2025Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: To compare the accuracy and readability of responses to oculoplastics patient questions provided by Google and ChatGPT. Additionally, to assess the ability of ChatGPT to create customized patient education materials. Methods: We executed a Google search to identify the 3 most frequently asked patient questions (FAQs) related to 10 oculoplastics conditions. FAQs were entered into both the Google search engine and the ChatGPT tool and responses were recorded. Responses were graded for readability using five validated readability indices and for accuracy by six oculoplastics surgeons. ChatGPT was instructed to create patient education materials at various reading levels for 8 oculoplastics procedures. The accuracy and readability of ChatGPT-generated procedural explanations were assessed. Results: ChatGPT responses to patient FAQs were written at a significantly higher average grade level than Google responses (grade 15.6 vs 10.0, p < 0.001). ChatGPT responses (93% accuracy) were significantly more accurate (p < 0.001) than Google responses (78% accuracy) and were preferred by expert panelists (79%). ChatGPT accurately explained oculoplastics procedures at an above average reading level. When instructed to rewrite patient education materials at a lower reading level, grade level was reduced by approximately 4 (15.7 vs 11.7, respectively, p < 0.001) without sacrificing accuracy. Conclusion: ChatGPT has the potential to provide patients with accurate information regarding their oculoplastics conditions. ChatGPT may also be utilized by oculoplastic surgeons as an accurate tool to provide customizable patient education for patients with varying health literacy. A better understanding of oculoplastics conditions and procedures amongst patients can lead to informed eye care decisions.
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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.