{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T04:26:44Z","timestamp":1772080004608,"version":"3.50.1"},"reference-count":23,"publisher":"Public Library of Science (PLoS)","issue":"4","license":[{"start":{"date-parts":[[2025,4,16]],"date-time":"2025-04-16T00:00:00Z","timestamp":1744761600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Japan glaucoma society"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:sec>\n<jats:title>Purpose<\/jats:title>\n<jats:p>To examine the influence of artificial intelligence (AI) on physicians\u2019 judgments regarding the presence and severity of glaucoma on fundus photographs in an online simulation system.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title>Methods<\/jats:title>\n<jats:p>Forty-five trainee and expert ophthalmologists independently evaluated 120 fundus photographs, including 30 photographs each from patients with no glaucoma, mild glaucoma, moderate glaucoma, and severe glaucoma. A second trial was conducted at least one week after the initial trial in which photograph presentation order was randomized. During the second trial, 30% of the glaucoma judgments made by the AI system were intentionally incorrect. The evaluators were asked about their thoughts on AI in ophthalmology via a 3-item questionnaire.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title>Results<\/jats:title>\n<jats:p>The percentage of correct responses for all images significantly improved (P &lt; 0.001) from 48.4 \u00b1 24.8% in the initial trial to 59.6 \u00b1 20.3% in the second trial. The improvement in the correct response rate was significantly greater for trainees (14.2 \u00b1 19.0%) than for experts (8.6 \u00b1 11.4%) (P = 0.04). The correct response rate was 63.9 \u00b1 20.6% when the AI response was correct, significantly greater than the 47.9 \u00b1 26.6% when the AI response was incorrect (P &lt; 0.0001). For trainees, the correct response rate was significantly greater when the AI\u2019s response was correct than when it was incorrect. However, for experts, the effect was less pronounced. The decision time was significantly longer when the AI response was incorrect than when it was correct (P = 0.003).<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title>Conclusion<\/jats:title>\n<jats:p>In fundus photography-based glaucoma detection, the results of AI systems can influence physicians\u2019 judgments, particularly those of physicians with less experience.<\/jats:p>\n<\/jats:sec>","DOI":"10.1371\/journal.pone.0321368","type":"journal-article","created":{"date-parts":[[2025,4,16]],"date-time":"2025-04-16T17:39:11Z","timestamp":1744825151000},"page":"e0321368","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":2,"title":["Influence of artificial intelligence on ophthalmologists\u2019 judgments in 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