2026-10-14T20:18:34Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33919582 2026-09-18T04:30:59Z category_393 portal_316 item_type_3 month_year_09_2026

Data Sheet 1_Effect of AI-driven simulation integrated with virtual case-based training on diagnostic and therapeutic reasoning homogenization among junior ophthalmologists: a prospective cohort study.docx Zhipeng Wu (3156408) Long Li (6555) Foetal Development and Medicine artificial intelligence clinical decision-making diabetic retinopathy diagnostic reasoning homogenization simulation-based medical education specialty training virtual patient training Background<p>Conventional training models for diabetic retinopathy (DR) inadequately address the dynamic decision-making demands required for diagnostic reasoning standardization among junior ophthalmologists.</p>Objective<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.</p>Methods<p>This single-center prospective cohort study enrolled 100 junior ophthalmologists (1–5 years’ clinical experience) assigned to an AI-integrated cohort (n = 50) or conventional cohort (n = 50). 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.</p>Results<p>Post-intervention, participants in the AI-integrated cohort showed significantly superior scheme-decision Kappa values (0.93 vs. 0.52; p < 0.001), key decision-point concordance (95.5% vs. 59.0%; p < 0.001), and differential diagnosis coverage (97.0% vs. 62.0%; p < 0.001). Diagnostic confidence (4.78 vs. 3.56; p < 0.001), complex-case diagnostic accuracy (94.0% vs. 60.0%; p < 0.001), and prognostic concordance with an independently validated reference model (RMSE: 0.31 vs. 1.52; p < 0.001) were markedly superior. Acute-complication response time was halved (11.4 vs. 21.3 min; p < 0.001), and high-risk-factor discrimination reached the high-discrimination classification (AUC: 0.95 vs. 0.72; p < 0.001). Individualized treatment-plan adaptation (90.4 ± 6.2 vs. 64.8 ± 7.4 on a 0–100 scale; p < 0.001) and comorbidity-association recognition (95.0% vs. 63.0%; p < 0.001) confirmed comprehensive practice competency gains. All advantages persisted at one-month follow-up (all p < 0.001), with cross-center consultation Kappa of 0.89 versus 0.49 and follow-up management accuracy of 92.0% versus 58.0%.</p>Conclusion<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.</p> 2026-09-18T04:30:59Z Dataset Dataset 10.3389/fmed.2026.1812146.s001 https://figshare.com/articles/dataset/Data_Sheet_1_Effect_of_AI-driven_simulation_integrated_with_virtual_case-based_training_on_diagnostic_and_therapeutic_reasoning_homogenization_among_junior_ophthalmologists_a_prospective_cohort_study_docx/33919582 CC BY 4.0