{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,25]],"date-time":"2026-10-25T17:46:41Z","timestamp":1792950401748,"version":"4.5.0"},"reference-count":25,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,1,4]],"date-time":"2026-01-04T00:00:00Z","timestamp":1767484800000},"content-version":"vor","delay-in-days":3,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Veterinary Ophthalmology"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To compare two large language models (ChatGPT\u20105, Gemini 2.5 Pro) with experienced and novice veterinarians on canine and feline fundus cases, and to assess the relationship between perceived case difficulty and diagnostic performance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Animals Studied<\/jats:title>\n                    <jats:p>Forty\u2010three client\u2010owned cases were sampled from 200 ophthalmology records.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Procedure(s)<\/jats:title>\n                    <jats:p>\n                      Each case included signalment, history, and fundus photographs. Two experienced veterinarians, two novice veterinarians, and two LLMs independently selected findings and provided diagnosis from options. Participants rated difficulty (Very Easy\u2013Hard). Group differences were tested with Kruskal\u2013Wallis and Dunn\u2010Bonferroni procedures; associations with difficulty used Spearman's\n                      <jats:italic>\u03c1<\/jats:italic>\n                      ; paired proportions used Cochran's Q with Holm\u2010adjusted McNemar tests.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Experts achieved the highest accuracies (findings: 73.3% and 61.6%; diagnosis: 86.0% and 66.3%), significantly outperforming LLMs and novices (all adjusted\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.05). LLM finding accuracies were 52.0% (ChatGPT\u20105) and 49.3% (Gemini 2.5 Pro), both above novices (28.3% and 26.9%). LLM diagnosis accuracies were lower (ChatGPT\u20105: 37.2%, Gemini 2.5 Pro: 37.2%) but still numerically higher than novices (23.1% and 22.5%). Expert accuracy declined with increasing case difficulty, whereas LLM performance was comparatively stable (ChatGPT\u20105 range 2.37\u20133.86; Gemini 2.5 Pro 2.00\u20132.95). Difficulty correlated negatively with Expert 2 totals (\n                      <jats:italic>\u03c1<\/jats:italic>\n                      \u2009=\u2009\u22120.70,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.0001) but not with LLMs (|\n                      <jats:italic>\u03c1<\/jats:italic>\n                      |\u2009\u2264\u20090.17,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009\u2265\u20090.28).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Experienced veterinarians are most accurate in fundus interpretation, but their performance declines with increasing difficulty. LLMs, though less accurate, remain stable across cases and outperform novices, indicating value as training or decision\u2010support tools. Future studies should assess whether expert\u2010LLM collaboration enhances accuracy and efficiency.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/vop.70138","type":"journal-article","created":{"date-parts":[[2026,1,4]],"date-time":"2026-01-04T10:41:27Z","timestamp":1767523287000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Observer\u2010Performance Comparison of\n                    <scp>ChatGPT<\/scp>\n                    \u20105 and Gemini 2.5 Pro Versus Veterinarians in Canine and Feline Fundus Interpretation: A Multi\u2010Reader, Multi\u2010Case Study"],"prefix":"10.1111","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1490-8832","authenticated-orcid":false,"given":"B\u00fc\u015fra","family":"Kibar","sequence":"first","affiliation":[{"name":"Faculty of Veterinary Medicine, Department of Surgery Ayd\u0131n Adnan Menderes University  Ayd\u0131n T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4184-8879","authenticated-orcid":false,"given":"\u00d6mer Tar\u0131k","family":"Orhun","sequence":"additional","affiliation":[{"name":"Faculty of Veterinary Medicine, Department of Surgery Necmettin Erbakan University  Konya T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-8394-003X","authenticated-orcid":false,"given":"Tu\u011f\u00e7e","family":"Kartal","sequence":"additional","affiliation":[{"name":"Faculty of Veterinary Medicine, Department of Surgery Atat\u00fcrk University  Erzurum T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-2787-1249","authenticated-orcid":false,"given":"B\u00fc\u015fra","family":"Baykal","sequence":"additional","affiliation":[{"name":"Faculty of Veterinary Medicine, Department of Surgery Atat\u00fcrk University  Erzurum 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