{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T05:05:34Z","timestamp":1789707934180,"version":"4.0.1"},"reference-count":28,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T00:00:00Z","timestamp":1789689600000},"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. Med."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Conventional training models for diabetic retinopathy (DR) inadequately address the dynamic decision-making demands required for diagnostic reasoning standardization among junior ophthalmologists.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To evaluate whether AI-driven simulation integrated with virtual DR case training is associated with greater homogenization of diagnostic and therapeutic reasoning compared with conventional case-based instruction.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      This single-center prospective cohort study enrolled 100 junior ophthalmologists (1\u20135 years\u2019 clinical experience) assigned to an AI-integrated cohort (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200950) or conventional cohort (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200950). Both underwent 12-week, 48-hour curricula. Outcomes were assessed at baseline, post-intervention, and one-month post-intervention using blinded expert evaluation and algorithmic benchmarking.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Post-intervention, participants in the AI-integrated cohort showed significantly superior scheme-decision Kappa values (0.93 vs. 0.52;\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&amp;lt;\u20090.001), key decision-point concordance (95.5% vs. 59.0%;\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&amp;lt;\u20090.001), and differential diagnosis coverage (97.0% vs. 62.0%;\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&amp;lt;\u20090.001). Diagnostic confidence (4.78 vs. 3.56;\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&amp;lt;\u20090.001), complex-case diagnostic accuracy (94.0% vs. 60.0%;\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&amp;lt;\u20090.001), and prognostic concordance with an independently validated reference model (RMSE: 0.31 vs. 1.52;\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&amp;lt;\u20090.001) were markedly superior. Acute-complication response time was halved (11.4 vs. 21.3\u2005min;\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&amp;lt;\u20090.001), and high-risk-factor discrimination reached the high-discrimination classification (AUC: 0.95 vs. 0.72;\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&amp;lt;\u20090.001). Individualized treatment-plan adaptation (90.4\u2009\u00b1\u20096.2 vs. 64.8\u2009\u00b1\u20097.4 on a 0\u2013100 scale;\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&amp;lt;\u20090.001) and comorbidity-association recognition (95.0% vs. 63.0%;\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&amp;lt;\u20090.001) confirmed comprehensive practice competency gains. All advantages persisted at one-month follow-up (all\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&amp;lt;\u20090.001), with cross-center consultation Kappa of 0.89 versus 0.49 and follow-up management accuracy of 92.0% versus 58.0%.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>AI-driven simulation integrated with virtual DR case training was associated with significantly enhanced reasoning homogenization, clinical confidence, and practical competency among junior ophthalmologists, suggesting that this integrated approach may offer a scalable, guideline-anchored model for standardized ophthalmic specialty training.<\/jats:p>\n                  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