ArticleFrontiers in public health2025
Comparative performance of large language models for patient-initiated ophthalmology consultations.
Article in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Large language models for primary care ophthalmic education: a systematic review.Frontiers in medicine · 2026Pooled it
- Comparison of Two AI Chatbots for Diagnosis and Providing Treatment Suggestions in Retinopathy of Prematurity: Retrospective Study.Journal of medical Internet research · 2026Article
- Generative artificial intelligence to augment ethical problem solving in ophthalmology: GPT-5.1 versus a human ethicist.International ophthalmology · 2026Article
- Exploring large language models as a prescription decision support tool for rational antibiotic use: A dual-framework analysis using standardized examinations and real-world clinical cases.Exploratory research in clinical and social pharmacy · 2026Article
- Comparative Performance of Gemini 3 Pro and GPT-5 Family Models on Ophthalmology Board-Style Questions.Ophthalmology science · 2026Article
- Patient education for neuromyelitis optica spectrum disorder using large language models: combining expert assessment and real-world patient interaction.Frontiers in physiology · 2026Article
- Benchmarking large language models for congenital cataract parent counseling: safety, readability, and knowledge translation of developmental and genetic information.Frontiers in cell and developmental biology · 2026Article
- Preoperative information needs and preferences of refractive surgery patients: a discrete choice experiment.Frontiers in medicine · 2026Article
- Comparative performance of GPT-4, GPT-o3, GPT-5, Gemini-3-Flash, and DeepSeek-R1 in ophthalmology question answering.Frontiers in cell and developmental biology · 2026Article
- Article
- Focus on sex hormone axis disorder: exploring the susceptibility mechanism of dry eye in perimenopausal women.Frontiers in medicine · 2026Review
- ChatGPT-5 versus other mainstream large language models in core diabetic retinopathy patient queries.Frontiers in cell and developmental biology · 2026Article
- Advances in the application of artificial intelligence in ophthalmic education and clinical training.Frontiers in medicine · 2025Review
- Evaluating large language model clinical reasoning in glaucoma using retrieval-augmented generation.Advances in ophthalmology practice and researchArticle
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Large language models (LLMs) are increasingly accessed by lay users for medical advice. This study aims to conduct a comprehensive evaluation of the responses generated by five large language models. Methods: We identified 31 ophthalmology-related questions most frequently raised by patients during routine consultations and subsequently elicited responses from five large language models: ChatGPT-4o, DeepSeek-V3, Doubao, Wenxin Yiyan 4.0 Turbo, and Qwen. A five-point likert scale was employed to assess each model across five domains: accuracy, logical consistency, coherence, safety, and content accessibility. Additionally, textual characteristics, including character, word, and sentence counts, were quantitatively analyzed. Results: ChatGPT-4o and DeepSeek-V3 achieved the highest overall performance, with statistically superior accuracy and logical consistency ( Conclusion: ChatGPT-4o and DeepSeek-V3 demonstrated the highest overall performance and are best suited for laypersons seeking ophthalmic information. Doubao and Qwen, with their richer clinical terminology, better serve users with medical training, whereas Wenxin Yiyan 4.0 Turbo most effectively supports patients' pre-procedural understanding of diagnostic procedures. Prospective randomized controlled trials are required to determine whether integrating the top-performing model into pre-consultation triage improves patient comprehension.
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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.