{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T19:04:59Z","timestamp":1790363099038,"version":"4.1.0"},"reference-count":25,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82171039"],"award-info":[{"award-number":["82171039"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["DIGITAL HEALTH"],"published-print":{"date-parts":[[2026,2]]},"abstract":"<jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To evaluate the performance of three large language models (LLMs) in automated recognition of IOLMaster 700 reports and preoperative toric intraocular lens (IOL) planning.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The retrospective study analyzed preoperative examination reports of patients who underwent cataract surgery with toric IOL implantation. Three models (ChatGPT-5, ChatGPT-5 Thinking and DeepSeek Thinking) were instructed to extract key biometric parameters, evaluate a patient\u2019s suitability for toric IOL implantation, and generate a plan. Model performance was evaluated based on structured-data recognition, refractive prediction outcomes and thinking times.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Fifty-four eyes of 54 patients were analyzed. ChatGPT-5 Thinking model consistently achieved the highest agreement with clinical reference for all extracted parameters, and demonstrated more reliable extraction of axis information. ChatGPT-5 showed intermediate performance, while DeepSeek Thinking was the least consistent in axis-dependent fields but performed adequately for basic biometry. Refractive and axis prediction errors were smallest with ChatGPT-5 Thinking, yielding the largest proportion of cases within prespecified clinical thresholds and the highest concordance with the calculator-based reference plan. Analysis of thinking times showed that longer processing did not necessarily correlate with better accuracy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Advanced LLMs show promise for automated interpretation of ophthalmic biometry reports and calculator-based toric IOL planning workflows. These findings support the feasibility of LLM-assisted workflow automation, with ChatGPT-5 Thinking providing the most favorable balance of accuracy and efficiency in this setting.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/20552076261444617","type":"journal-article","created":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T15:15:43Z","timestamp":1776352543000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":3,"title":["Comparison of three large language models in recognizing ophthalmological examination and supporting preoperative toric IOL planning"],"prefix":"10.1177","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-4278-290X","authenticated-orcid":false,"given":"Xuanqiao","family":"Lin","sequence":"first","affiliation":[{"name":"Eye, Ear, Nose, and Throat Hospital of Fudan University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yizhou","family":"Yang","sequence":"additional","affiliation":[{"name":"Eye, Ear, Nose, and Throat Hospital of Fudan University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Songlian","family":"Wang","sequence":"additional","affiliation":[{"name":"University of Shanghai for Science and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Cai","sequence":"additional","affiliation":[{"name":"Eye, Ear, Nose, and Throat Hospital of Fudan University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3556-0046","authenticated-orcid":false,"given":"Jin","family":"Yang","sequence":"additional","affiliation":[{"name":"Eye, Ear, Nose, and Throat Hospital of Fudan University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,4,16]]},"reference":[{"key":"e_1_3_6_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ophtha.2017.04.014"},{"issue":"4","key":"e_1_3_6_3_2","first-page":"308","article-title":"The Continuing Evolution of Cataract Surgery","volume":"6","author":"Chang DF","year":"2017","unstructured":"Chang DF. The Continuing Evolution of Cataract Surgery. Asia Pac J Ophthalmol (Phila) 2017; 6(4): 308. https:\/\/doi.org\/10.22608\/APO.2017191","journal-title":"Asia Pac J Ophthalmol (Phila)"},{"key":"e_1_3_6_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcrs.2012.08.060"},{"key":"e_1_3_6_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcrs.2012.07.025"},{"key":"e_1_3_6_6_2","doi-asserted-by":"publisher","DOI":"10.4103\/ijo.IJO_810_17"},{"key":"e_1_3_6_7_2","doi-asserted-by":"publisher","DOI":"10.3928\/1081597X-20200729-03"},{"key":"e_1_3_6_8_2","doi-asserted-by":"publisher","DOI":"10.3928\/1081597X-20250930-02"},{"key":"e_1_3_6_9_2","doi-asserted-by":"publisher","DOI":"10.1097\/00055735-200002000-00007"},{"key":"e_1_3_6_10_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-024-03139-8"},{"key":"e_1_3_6_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajo.2024.05.022"},{"issue":"4","key":"e_1_3_6_12_2","first-page":"351","article-title":"Using a large language model to process biometry reports and select intraocular lens for cataract surgery","volume":"51","author":"Tan JCK","year":"2025","unstructured":"Tan JCK. Using a large language model to process biometry reports and select intraocular lens for cataract surgery. J Cataract Refract Surg 2025; 51(4): 351\u2013352. https:\/\/doi.org\/10.1097\/j.jcrs.0000000000001620","journal-title":"J Cataract Refract Surg"},{"key":"e_1_3_6_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajoint.2025.100205"},{"key":"e_1_3_6_14_2","doi-asserted-by":"publisher","DOI":"10.4103\/IJO.IJO_1350_23"},{"key":"e_1_3_6_15_2","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2023.1291404"},{"key":"e_1_3_6_16_2","doi-asserted-by":"publisher","DOI":"10.3389\/frai.2025.1548385"},{"key":"e_1_3_6_17_2","doi-asserted-by":"publisher","DOI":"10.1111\/opo.13284"},{"key":"e_1_3_6_18_2","doi-asserted-by":"publisher","DOI":"10.1097\/j.jcrs.0000000000000560"},{"key":"e_1_3_6_19_2","doi-asserted-by":"publisher","DOI":"10.2147\/OPTH.S448427"},{"key":"e_1_3_6_20_2","volume-title":"Toric Intraocular Lenses. StatPearls. 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Int J Ophthalmol 2015; 8(4): 719\u2013724. https:\/\/doi.org\/10.3980\/j.issn.2222-3959.2015.04.14","journal-title":"Int J Ophthalmol"},{"key":"e_1_3_6_22_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajo.2011.08.014"},{"key":"e_1_3_6_23_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajo.2025.02.028"},{"issue":"4","key":"e_1_3_6_24_2","first-page":"332","article-title":"Evaluation of an artificial intelligence-based intraocular lens calculator: AI-based IOL-optimized formula","volume":"51","author":"Wang L","year":"2024","unstructured":"Wang L, Burwinkel H, Bensaid N, et al. Evaluation of an artificial intelligence-based intraocular lens calculator: AI-based IOL-optimized formula. 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