{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T17:48:44Z","timestamp":1789148924506,"version":"build-2803163510"},"reference-count":32,"publisher":"Elsevier BV","issue":"5","license":[{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T00:00:00Z","timestamp":1773705600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100006808","name":"University of North Carolina","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100006808","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007890","name":"University of North Carolina at Chapel Hill","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007890","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100015505","name":"Odum Institute for Research in Social Science, University of North Carolina at Chapel Hill","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100015505","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","ophthalmologyscience.org","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Ophthalmology Science"],"published-print":{"date-parts":[[2026,5]]},"DOI":"10.1016\/j.xops.2026.101170","type":"journal-article","created":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T16:01:39Z","timestamp":1774108899000},"page":"101170","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"title":["Comprehensive Evaluation of ChatGPT\u2019s Diagnostic Accuracy on Image-based Ophthalmic Case Interpretations"],"prefix":"10.1016","volume":"6","author":[{"given":"Cheng","family":"Jiao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-4140-696X","authenticated-orcid":false,"given":"Iden","family":"Amiri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5194-5256","authenticated-orcid":false,"given":"Alice Yang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.xops.2026.101170_bib1","doi-asserted-by":"crossref","DOI":"10.1016\/j.xops.2024.100600","article-title":"Analysis of ChatGPT responses to ophthalmic cases: can ChatGPT think like an ophthalmologist?","volume":"5","author":"Chen","year":"2025","journal-title":"Ophthalmol Sci"},{"key":"10.1016\/j.xops.2026.101170_bib2","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-023-45837-2","article-title":"Comparative performance of humans versus GPT-4.0 and GPT-3.5 in the self-assessment program of American Academy of Ophthalmology","volume":"13","author":"Taloni","year":"2023","journal-title":"Sci Rep"},{"key":"10.1016\/j.xops.2026.101170_bib3","doi-asserted-by":"crossref","DOI":"10.1016\/j.xops.2024.100667","article-title":"Glaucoma detection and feature identification via GPT-4V fundus image analysis","volume":"5","author":"Jalili","year":"2025","journal-title":"Ophthalmol Sci"},{"key":"10.1016\/j.xops.2026.101170_bib4","doi-asserted-by":"crossref","first-page":"3121","DOI":"10.1007\/s40123-023-00805-x","article-title":"The use of ChatGPT to assist in diagnosing glaucoma based on clinical case reports","volume":"12","author":"Delsoz","year":"2023","journal-title":"Ophthalmol Ther"},{"key":"10.1016\/j.xops.2026.101170_bib5","doi-asserted-by":"crossref","DOI":"10.1001\/jamanetworkopen.2024.37711","article-title":"Performance of multimodal artificial intelligence chatbots evaluated on clinical oncology cases","volume":"7","author":"Chen","year":"2024","journal-title":"JAMA Netw Open"},{"key":"10.1016\/j.xops.2026.101170_bib7","doi-asserted-by":"crossref","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":"10.1016\/j.xops.2026.101170_bib8","article-title":"Evaluating large language models in ophthalmology","author":"Holmes","year":"2023","journal-title":"arXiv"},{"key":"10.1016\/j.xops.2026.101170_bib9","doi-asserted-by":"crossref","DOI":"10.1155\/2018\/5278196","article-title":"Applications of artificial intelligence in ophthalmology: general overview","volume":"2018","author":"Lu","year":"2018","journal-title":"J Ophthalmol"},{"key":"10.1016\/j.xops.2026.101170_bib10","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1136\/bjophthalmol-2021-319030","article-title":"Accuracy of automated machine learning in classifying retinal pathologies from ultra-widefield pseudocolour fundus images","volume":"107","author":"Antaki","year":"2023","journal-title":"Br J Ophthalmol"},{"key":"10.1016\/j.xops.2026.101170_bib11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.preteyeres.2018.07.004","article-title":"Artificial intelligence in retina","volume":"67","author":"Schmidt-Erfurth","year":"2018","journal-title":"Prog Retin Eye Res"},{"key":"10.1016\/j.xops.2026.101170_bib12","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.ajo.2023.05.024","article-title":"Performance of generative large language models on ophthalmology board-style questions","volume":"254","author":"Cai","year":"2023","journal-title":"Am J Ophthalmol"},{"key":"10.1016\/j.xops.2026.101170_bib13","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijmedinf.2025.105886","article-title":"Using large language models as decision support tools in emergency ophthalmology","volume":"199","author":"Kreso","year":"2025","journal-title":"Int J Med Inf"},{"key":"10.1016\/j.xops.2026.101170_bib14","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1007\/s10792-025-03602-6","article-title":"Advanced glaucoma disease segmentation and classification with grey wolf optimized U-Net++ and capsule networks","volume":"45","author":"Govindharaj","year":"2025","journal-title":"Int Ophthalmol"},{"key":"10.1016\/j.xops.2026.101170_bib15","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1007\/s10792-024-03391-4","article-title":"Capsule network\u2013based deep learning for early and accurate diabetic retinopathy detection","volume":"45","author":"Govindharaj","year":"2024","journal-title":"Int Ophthalmol"},{"key":"10.1016\/j.xops.2026.101170_bib16","article-title":"Grey wolf optimization technique with U-shaped and capsule networks for glaucoma diagnosis","volume":"14","author":"Govindharaj","year":"2025","journal-title":"MethodsX"},{"key":"10.1016\/j.xops.2026.101170_bib17","article-title":"Enhanced diabetic retinopathy detection using U-shaped network and capsule network\u2013driven deep learning","volume":"13","author":"Govindharaj","year":"2024","journal-title":"MethodsX"},{"key":"10.1016\/j.xops.2026.101170_bib18","article-title":"Enhancing glaucoma diagnosis using GAN-synthesized imagery and pretrained MobileNetV2 classification","volume":"13","author":"Govindharaj","year":"2024","journal-title":"MethodsX"},{"key":"10.1016\/j.xops.2026.101170_bib19","article-title":"Physicians' perspectives on ChatGPT in ophthalmology: insights on Artificial Intelligence (AI) integration in clinical practice","volume":"17","author":"Ahmed","year":"2025","journal-title":"Cureus"},{"key":"10.1016\/j.xops.2026.101170_bib20","first-page":"649","article-title":"How to use large language models in ophthalmology: from prompt engineering to protecting confidentiality","volume":"38","author":"Kleinig","year":"2024","journal-title":"Eye Lond Engl"},{"key":"10.1016\/j.xops.2026.101170_bib21","doi-asserted-by":"crossref","DOI":"10.1186\/s13054-023-04473-y","article-title":"Artificial intelligence hallucinations","volume":"27","author":"Salvagno","year":"2023","journal-title":"Crit Care Lond Engl"},{"key":"10.1016\/j.xops.2026.101170_bib22","doi-asserted-by":"crossref","DOI":"10.1001\/jamanetworkopen.2023.30320","article-title":"Comparison of ophthalmologist and large Language model chatbot responses to online patient eye care questions","volume":"6","author":"Bernstein","year":"2023","journal-title":"JAMA Netw Open"},{"key":"10.1016\/j.xops.2026.101170_bib23","doi-asserted-by":"crossref","first-page":"589","DOI":"10.1001\/jamainternmed.2023.1838","article-title":"Comparing physician and artificial intelligence chatbot responses to patient questions posted to a public social media forum","volume":"183","author":"Ayers","year":"2023","journal-title":"JAMA Intern Med"},{"key":"10.1016\/j.xops.2026.101170_bib24","doi-asserted-by":"crossref","DOI":"10.1016\/j.xops.2023.100324","article-title":"Evaluating the performance of ChatGPT in ophthalmology: an analysis of its successes and shortcomings","volume":"3","author":"Antaki","year":"2023","journal-title":"Ophthalmol Sci"},{"key":"10.1016\/j.xops.2026.101170_bib25","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pdig.0000198","article-title":"Performance of ChatGPT on USMLE: potential for AI-assisted medical education using large language models","volume":"2","author":"Kung","year":"2023","journal-title":"PLoS Digit Health"},{"key":"10.1016\/j.xops.2026.101170_bib26","doi-asserted-by":"crossref","first-page":"e107","DOI":"10.1016\/S2589-7500(23)00021-3","article-title":"ChatGPT: the future of discharge summaries?","volume":"5","author":"Patel","year":"2023","journal-title":"Lancet Digit Health"},{"key":"10.1016\/j.xops.2026.101170_bib27","doi-asserted-by":"crossref","DOI":"10.1002\/ctm2.1324","article-title":"The application of ChatGPT in healthcare progress notes: a commentary from a clinical and research perspective","volume":"13","author":"Nguyen","year":"2023","journal-title":"Clin Transl Med"},{"key":"10.1016\/j.xops.2026.101170_bib28","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1136\/bjophthalmol-2018-313173","article-title":"Artificial intelligence and deep learning in ophthalmology","volume":"103","author":"Ting","year":"2019","journal-title":"Br J Ophthalmol"},{"key":"10.1016\/j.xops.2026.101170_bib29","doi-asserted-by":"crossref","first-page":"892","DOI":"10.1038\/s41591-020-0867-7","article-title":"Predicting conversion to wet age-related macular degeneration using deep learning","volume":"26","author":"Yim","year":"2020","journal-title":"Nat Med"},{"key":"10.1016\/j.xops.2026.101170_bib30","doi-asserted-by":"crossref","first-page":"1342","DOI":"10.1038\/s41591-018-0107-6","article-title":"Clinically applicable deep learning for diagnosis and referral in retinal disease","volume":"24","author":"De Fauw","year":"2018","journal-title":"Nat Med"},{"key":"10.1016\/j.xops.2026.101170_bib31","doi-asserted-by":"crossref","first-page":"889","DOI":"10.1136\/bjophthalmol-2022-321141","article-title":"New meaning for NLP: the trials and tribulations of natural language processing with GPT-3 in ophthalmology","volume":"106","author":"Nath","year":"2022","journal-title":"Br J Ophthalmol"},{"key":"10.1016\/j.xops.2026.101170_bib32","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1038\/s41586-023-06291-2","article-title":"Large language models encode clinical knowledge","volume":"620","author":"Singhal","year":"2023","journal-title":"Nature"},{"key":"10.1016\/j.xops.2026.101170_bib33","article-title":"Can large language models reason about medical questions?","author":"Li\u00e9vin","year":"2023","journal-title":"arXiv"}],"container-title":["Ophthalmology Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2666914526001089?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2666914526001089?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T09:38:55Z","timestamp":1778924335000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2666914526001089"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5]]},"references-count":32,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2026,5]]}},"alternative-id":["S2666914526001089"],"URL":"https:\/\/doi.org\/10.1016\/j.xops.2026.101170","relation":{},"ISSN":["2666-9145"],"issn-type":[{"value":"2666-9145","type":"print"}],"subject":[],"published":{"date-parts":[[2026,5]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Comprehensive Evaluation of ChatGPT\u2019s Diagnostic Accuracy on Image-based Ophthalmic Case Interpretations","name":"articletitle","label":"Article Title"},{"value":"Ophthalmology Science","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.xops.2026.101170","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 American Academy of Ophthalmology, Inc. Published by Elsevier Inc.","name":"copyright","label":"Copyright"}],"article-number":"101170"}}