{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:10:55Z","timestamp":1787026255995,"version":"build-2736575974"},"reference-count":46,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T00:00:00Z","timestamp":1774396800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Cell Dev. Biol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Diabetic retinopathy is a leading cause of preventable vision loss, and patients increasingly seek disease related information through online consultations. Large language models may support patient education, but their reliability and usability vary across systems, particularly in disease specific settings.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Thirty common patient questions about diabetic retinopathy were developed from guidelines and organized into five domains: disease overview, screening and diagnosis, treatment and follow up, lifestyle and prevention, and prognosis and complication management. From November 10 to 15, 2025, two researchers independently submitted all questions to five models (ChatGPT-5, DeepSeek-V3.1, Doubao, Wenxinyiyan 4.5 Turbo, and Kimi) on public platforms under identical conditions without system prompts. Chat histories were reset before each question. Response time, response length, structural metrics, and table outputs were extracted. Two retinal specialists rated each answer on a 1 to 5 Likert scale across accuracy, logical consistency, coherence, safety, and content accessibility. Inter rater agreement was assessed with the intraclass correlation coefficient. Group differences were analyzed using analysis of variance or the Kruskal\u2013Wallis H test with Bonferroni corrected pairwise comparisons.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Significant between model differences were observed in output efficiency and textual characteristics (all P &amp;lt; 0.001). ChatGPT-5 responded fastest (15.92 \u00b1 4.48\u00a0s), whereas Wenxinyiyan 4.5 Turbo and DeepSeek-V3.1 were slowest (41.89 \u00b1 5.09\u00a0s and 38.20 \u00b1 2.96\u00a0s). DeepSeek-V3.1 generated the longest answers (1396.37 \u00b1 189.23 words), while Kimi produced the shortest (579.40 \u00b1 182.96 words). Only ChatGPT-5 consistently generated structured tables (median 2.00, IQR 1.00-2.00). Content quality differed significantly across all five dimensions (H = 15.34-37.19, all P \u2264 0.004). ChatGPT-5 achieved the highest median scores for accuracy (5.00, IQR 4.00-5.00) and logical consistency (4.50, IQR 4.00-5.00), whereas Kimi showed the lowest accuracy (3.50, IQR 3.00-4.00). The intraclass correlation coefficient indicated good inter rater reliability (0.87).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Performance of large language models in diabetic retinopathy patient consultations is model dependent. ChatGPT-5 demonstrated the best overall usability, combining faster responses, clearer structure, and higher factual accuracy. Other Chinese optimized models provided comparable professional information coverage but require improved accessibility and stability for safe patient facing use.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fcell.2026.1754221","type":"journal-article","created":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T06:51:37Z","timestamp":1774421497000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["ChatGPT-5 versus other mainstream large language models in core diabetic retinopathy patient queries"],"prefix":"10.3389","volume":"14","author":[{"given":"Xiaomin","family":"Cang","sequence":"first","affiliation":[{"name":"Department of Endocrinology, Nantong First People\u2019s Hospital, Southeast University","place":["Nantong, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengxia","family":"Ni","sequence":"additional","affiliation":[{"name":"Hai\u2019an High-tech District Central Health Center","place":["Nantong, Jiangsu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunyan","family":"Song","sequence":"additional","affiliation":[{"name":"Shenzhen Eye Hospital, Shenzhen Eye Medical Center, Southern Medical University","place":["Shenzhen, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jialuo","family":"Zhao","sequence":"additional","affiliation":[{"name":"Shenzhen Eye Hospital, Shenzhen Eye Medical Center, Southern Medical University","place":["Shenzhen, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingxin","family":"Guo","sequence":"additional","affiliation":[{"name":"Shenzhen Eye Hospital, Shenzhen Eye Medical Center, Southern Medical University","place":["Shenzhen, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunyun","family":"Zou","sequence":"additional","affiliation":[{"name":"Shenzhen Eye Hospital, Shenzhen Eye Medical Center, Southern Medical University","place":["Shenzhen, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhe","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shenzhen Eye Hospital, Shenzhen Eye Medical Center, Southern Medical University","place":["Shenzhen, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ligang","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Quzhou People\u2019s Hospital, Quzhou Affiliated Hospital of Wenzhou Medical University","place":["Quzhou, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,3,25]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"1371","DOI":"10.1136\/bjo-2023-324438","article-title":"Capabilities of GPT-4 in ophthalmology: an analysis of model entropy and progress towards human-level medical question answering","volume":"108","author":"Antaki","year":"2024","journal-title":"Br. J. Ophthalmol."},{"key":"B2","doi-asserted-by":"publisher","first-page":"e917","DOI":"10.1016\/S2589-7500(23)00201-7","article-title":"Large language models and their impact in ophthalmology","volume":"5","author":"Betzler","year":"2023","journal-title":"Lancet Digit. Health"},{"key":"B3","doi-asserted-by":"publisher","first-page":"475","DOI":"10.1016\/j.ophtha.2020.07.043","article-title":"Economic challenges of artificial intelligence adoption for diabetic retinopathy","volume":"128","author":"Chen","year":"2020","journal-title":"OPHTHALMOLOGY"},{"key":"B4","doi-asserted-by":"publisher","first-page":"S321","DOI":"10.2337\/dc25-S016","article-title":"Diabetes care in the hospital: standards of care in Diabetes-2025","volume":"48","year":"2025","journal-title":"Diabetes Care"},{"key":"B5","doi-asserted-by":"publisher","first-page":"625","DOI":"10.1038\/s41586-024-07421-0","article-title":"Detecting hallucinations in large language models using semantic entropy","volume":"630","author":"Farquhar","year":"2024","journal-title":"NATURE"},{"key":"B6","doi-asserted-by":"publisher","first-page":"e662","DOI":"10.1016\/S2589-7500(24)00124-9","article-title":"A future role for health applications of large language models depends on regulators enforcing safety standards","volume":"6","author":"Freyer","year":"2024","journal-title":"Lancet Digit. Health"},{"key":"B7","doi-asserted-by":"publisher","first-page":"889","DOI":"10.1038\/d41586-025-02275-6","article-title":"Another DeepSeek moment': chinese AI model kimi K2 stirs excitement","volume":"643","author":"Gibney","year":"2025","journal-title":"Nature"},{"key":"B8","doi-asserted-by":"publisher","first-page":"e2242972","DOI":"10.1001\/jamanetworkopen.2022.42972","article-title":"Accuracy in patient understanding of common medical phrases","volume":"5","author":"Gotlieb","year":"2022","journal-title":"JAMA Netw. Open"},{"key":"B9","doi-asserted-by":"publisher","first-page":"Nat Commun","DOI":"10.1038\/s41467-024-55628-6","article-title":"Large language models lack essential metacognition for reliable medical reasoning","volume":"16","author":"Griot","year":"2025","journal-title":"Nat. Commun."},{"key":"B10","doi-asserted-by":"publisher","first-page":"3968","DOI":"10.1038\/s41591-025-04008-8","article-title":"The fragile intelligence of GPT-5 in medicine","volume":"31","author":"Handler","year":"2025","journal-title":"Nat. Med."},{"key":"B11","doi-asserted-by":"publisher","first-page":"EBioMedicine","DOI":"10.1016\/j.ebiom.2023.104512","article-title":"Attention is not all you need: the complicated case of ethically using large language models in healthcare and medicine","volume":"90","author":"Harrer","year":"2023","journal-title":"EBioMedicine"},{"key":"B12","doi-asserted-by":"publisher","first-page":"100089","DOI":"10.1016\/j.apjo.2024.100089","article-title":"A review of ophthalmology education in the era of generative artificial intelligence","volume":"13","author":"Heinke","year":"2024","journal-title":"Asia Pac J. Ophthalmol. (Phila)"},{"key":"B13","doi-asserted-by":"publisher","first-page":"4296","DOI":"10.1038\/s41467-023-39864-w","article-title":"Prevalence of diabetic retinopathy and vision-threatening diabetic retinopathy in adults with diabetes in China","volume":"14","author":"Hou","year":"2023","journal-title":"Nat. Commun."},{"key":"B14","doi-asserted-by":"publisher","first-page":"371","DOI":"10.1001\/jamaophthalmol.2023.6917","article-title":"Assessment of a large language model's responses to questions and cases about glaucoma and retina management","volume":"142","author":"Huang","year":"2024","journal-title":"JAMA Ophthalmol."},{"key":"B15","doi-asserted-by":"publisher","first-page":"1673045","DOI":"10.3389\/fpubh.2025.1673045","article-title":"Comparative performance of large language models for patient-initiated ophthalmology consultations","volume":"13","author":"Huang","year":"2025","journal-title":"Front. Public Health"},{"key":"B16","doi-asserted-by":"publisher","first-page":"e2457879","DOI":"10.1001\/jamanetworkopen.2024.57879","article-title":"Large language models for chatbot health advice studies: a systematic review","volume":"8","author":"Huo","year":"2025","journal-title":"JAMA Netw. Open"},{"key":"B17","doi-asserted-by":"publisher","first-page":"1532939","DOI":"10.3389\/fcell.2024.1532939","article-title":"Exploring the effect of gestational diabetes mellitus on retinal vascular morphology by PKSEA-net","volume":"12","author":"Jiang","year":"2025","journal-title":"Front. Cell Dev. Biol."},{"key":"B18","doi-asserted-by":"publisher","first-page":"e375","DOI":"10.1016\/j.jcjo.2024.08.010","article-title":"Prompt engineering with ChatGPT3.5 and GPT4 to improve patient education on retinal diseases","volume":"60","author":"Jung","year":"2024","journal-title":"Can. J. Ophthalmol."},{"key":"B19","doi-asserted-by":"publisher","first-page":"705","DOI":"10.1007\/s40123-025-01105-2","article-title":"Evaluating the efficacy of large language models in guiding treatment decisions for pediatric refractive error","volume":"14","author":"Kang","year":"2025","journal-title":"Ophthalmol. Ther."},{"key":"B20","doi-asserted-by":"publisher","first-page":"e50862","DOI":"10.2196\/50862","article-title":"Jargon and readability in plain language summaries of health research: cross-Sectional observational Study","volume":"27","author":"Lang","year":"2025","journal-title":"J. Med. Internet Res."},{"key":"B21","doi-asserted-by":"publisher","first-page":"1600202","DOI":"10.3389\/fcell.2025.1600202","article-title":"Large language model-based multimodal system for detecting and grading ocular surface diseases from smartphone images","volume":"13","author":"Li","year":"2025","journal-title":"Front. Cell Dev. Biol."},{"key":"B22","doi-asserted-by":"publisher","first-page":"P75","DOI":"10.1016\/j.ophtha.2024.12.020","article-title":"Diabetic retinopathy preferred practice pattern\u00ae","volume":"132","author":"Lim","year":"2025","journal-title":"OPHTHALMOLOGY"},{"key":"B23","doi-asserted-by":"publisher","first-page":"A4373036","DOI":"10.1161\/circ.152.suppl_3.4373036","article-title":"Abstract 4373036: comparative evaluation of US- and china-developed large language models for bilingual coronary heart disease patient","volume":"152","author":"Liu","year":"2025","journal-title":"Educ. Circ."},{"key":"B24","doi-asserted-by":"publisher","first-page":"747","DOI":"10.1001\/jamaophthalmol.2023.2289","article-title":"Prevalence of diabetic Retinopathy in the US in 2021","volume":"141","author":"Lundeen","year":"2023","journal-title":"JAMA Ophthalmol."},{"key":"B25","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1001\/jamaophthalmol.2025.2918","article-title":"DeepSeek-R1 vs OpenAI o1 for ophthalmic diagnoses and management plans","volume":"143","author":"Mikhail","year":"2025","journal-title":"JAMA Ophthalmol."},{"key":"B26","doi-asserted-by":"publisher","first-page":"1169","DOI":"10.1016\/j.ophtha.2020.12.019","article-title":"Severity of diabetic retinopathy and the risk of future cerebrovascular disease, cardiovascular disease, and all-cause mortality","volume":"128","author":"Modjtahedi","year":"2020","journal-title":"OPHTHALMOLOGY"},{"key":"B27","doi-asserted-by":"publisher","first-page":"1078","DOI":"10.1016\/j.jaad.2024.01.037","article-title":"Assessing the accuracy, usefulness, and readability of artificial-intelligence-generated responses to common dermatologic surgery questions for patient education: a double-blinded comparative study of ChatGPT and Google bard","volume":"90","author":"Robinson","year":"2024","journal-title":"J. Am. Acad. DERMATOL."},{"key":"B28","doi-asserted-by":"publisher","first-page":"2546","DOI":"10.1038\/s41591-025-03727-2","article-title":"Benchmark evaluation of DeepSeek large language models in clinical decision-making","volume":"31","author":"Sandmann","year":"2025","journal-title":"Nat. Med."},{"key":"B29","doi-asserted-by":"publisher","first-page":"592","DOI":"10.1038\/s41746-025-01943-1","article-title":"A longitudinal analysis of declining medical safety messaging in generative AI models","volume":"8","author":"Sharma","year":"2025","journal-title":"NPJ Digit. Med."},{"key":"B30","doi-asserted-by":"publisher","first-page":"1614","DOI":"10.1007\/s00125-023-05940-5","article-title":"What else can we do to prevent diabetic retinopathy?","volume":"66","author":"Sim\u00f3","year":"2023","journal-title":"DIABETOLOGIA"},{"key":"B31","doi-asserted-by":"publisher","first-page":"943","DOI":"10.1038\/s41591-024-03423-7","article-title":"Toward expert-level medical question answering with large language models","volume":"31","author":"Singhal","year":"2025","journal-title":"Nat. Med."},{"key":"B32","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1001\/jamaophthalmol.2025.2413","article-title":"Ophthalmological question answering and reasoning using OpenAI o1 vs other large language models","volume":"143","author":"Srinivasan","year":"2025","journal-title":"JAMA Ophthalmol."},{"key":"B33","doi-asserted-by":"publisher","first-page":"1580","DOI":"10.1016\/j.ophtha.2021.04.027","article-title":"Global prevalence of diabetic retinopathy and projection of burden through 2045: systematic review and meta-analysis","volume":"128","author":"Teo","year":"2021","journal-title":"OPHTHALMOLOGY"},{"key":"B34","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1136\/bjophthalmol-2018-313173","article-title":"Artificial intelligence and deep learning in ophthalmology","volume":"103","author":"Ting","year":"2018","journal-title":"Br. J. Ophthalmol."},{"key":"B35","doi-asserted-by":"publisher","first-page":"1134","DOI":"10.1038\/s41591-024-02855-5","article-title":"Adapted large language models can outperform medical experts in clinical text summarization","volume":"30","author":"Van Veen","year":"2024","journal-title":"Nat. Med."},{"key":"B36","doi-asserted-by":"publisher","first-page":"101206","DOI":"10.1016\/j.preteyeres.2023.101206","article-title":"Optical coherence tomography angiography in diabetic retinopathy","volume":"97","author":"Waheed","year":"2023","journal-title":"Prog. RETIN EYE Res."},{"key":"B37","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1136\/bjo-2024-325599","article-title":"Evaluating the effectiveness of large language models in patient education for conjunctivitis","volume":"109","author":"Wang","year":"2025","journal-title":"Br. J. Ophthalmol."},{"key":"B38","doi-asserted-by":"publisher","first-page":"922","DOI":"10.1093\/geroni\/igae098.2976","article-title":"Exploring the landscape of generative ai and large language models in alzheimer and dementias research","volume":"8","author":"Wei","year":"2024","journal-title":"Innov. Aging"},{"key":"B39","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1007\/s10916-025-02227-7","article-title":"Evaluating the performance of ChatGPT on board-style examination questions in ophthalmology: a meta-analysis","volume":"49","author":"Wei","year":"2025","journal-title":"J. Med. Syst."},{"key":"B40","doi-asserted-by":"publisher","first-page":"100106","DOI":"10.1016\/j.apjo.2024.100106","article-title":"Accuracy of large language models in answering ophthalmology board-style questions: a meta-analysis","volume":"13","author":"Wu","year":"2024","journal-title":"Asia Pac J. Ophthalmol. (Phila)"},{"key":"B41","doi-asserted-by":"publisher","first-page":"1272","DOI":"10.1136\/bjo-2024-325861","article-title":"Enhancing diabetic retinopathy query responses: assessing large language model in ophthalmology","volume":"109","author":"Wu","year":"2025","journal-title":"BRIT J. Ophthalmol."},{"key":"B42","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1038\/s41746-025-01533-1","article-title":"Medical foundation large language models for comprehensive text analysis and beyond","volume":"8","author":"Xie","year":"2025","journal-title":"NPJ Digit. Med."},{"key":"B43","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1038\/s41746-025-01468-7","article-title":"The use of large language models in detecting Chinese ultrasound report errors","volume":"8","author":"Yan","year":"2025","journal-title":"NPJ Digit. Med."},{"key":"B44","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/j.ophtha.2025.08.004","article-title":"The prevalence of diabetic retinopathy in American Indians or Alaska natives and non-indigenous Americans: a systematic review and meta-analysis","volume":"133","author":"Yu","year":"2025","journal-title":"OPHTHALMOLOGY"},{"key":"B45","doi-asserted-by":"publisher","first-page":"1019","DOI":"10.1016\/j.survophthal.2025.02.009","article-title":"Advancing ophthalmology with large language models: applications, challenges, and future directions","volume":"70","author":"Zhang","year":"2025","journal-title":"Surv. Ophthalmol."},{"key":"B46","doi-asserted-by":"publisher","first-page":"e76947","DOI":"10.2196\/76947","article-title":"Evaluating large language models in ophthalmology: systematic review","volume":"27","author":"Zhang","year":"2025","journal-title":"J. Med. INTERNET Res."}],"container-title":["Frontiers in Cell and Developmental Biology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fcell.2026.1754221\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T06:51:38Z","timestamp":1774421498000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fcell.2026.1754221\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,25]]},"references-count":46,"alternative-id":["10.3389\/fcell.2026.1754221"],"URL":"https:\/\/doi.org\/10.3389\/fcell.2026.1754221","relation":{},"ISSN":["2296-634X"],"issn-type":[{"value":"2296-634X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,25]]},"article-number":"1754221"}}