Table · dataset · 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
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Description
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).
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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>
Links
Where it is published
- DOI doi.org/10.3389/fmed.2026.1812146.s001 ↗
DOI / persistent id · from figshare com
Catalogue records · 1
- OAI-PMH record api.figshare.com/v2/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Af… ↗
metadata API · from figshare com
Topics
- From keywords
- Artificial intelligence · Astronomy & Astrophysics · Chemistry · Computer Science & AI · Earth & Environmental Science · Economics & Finance · Engineering · Foetal development and medicine · Humanities · Life Sciences · Medicine & Health · Ocean & Atmospheric Science · Social Science
- Inferred from text
- Longitudinal study 65% · Simulation 75%
Provenance · 1 source records, 20 field assertions
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|---|---|---|---|
| figshare | oai:figshare.com:article/33919582 | 12 d ago | JSON v1 |
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| concepts[field].anzsrc:group:4602 | mapping · figshare com | vocabulary-mapper@1.0.0 | keywords['artificial intelligence'] |
| concepts[field].local:field:astronomy | mapping · figshare com | connector:figshare_com@1.0.0 | |
| concepts[field].local:field:chemistry | mapping · figshare com | connector:figshare_com@1.0.0 | |
| concepts[field].local:field:computer-science-ai | mapping · figshare com | connector:figshare_com@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · figshare com | connector:figshare_com@1.0.0 | |
| concepts[field].local:field:economics-finance | mapping · figshare com | connector:figshare_com@1.0.0 | |
| concepts[field].local:field:engineering | mapping · figshare com | connector:figshare_com@1.0.0 | |
| concepts[field].local:field:humanities | mapping · figshare com | connector:figshare_com@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · figshare com | connector:figshare_com@1.0.0 | |
| concepts[field].local:field:medicine-health | mapping · figshare com | connector:figshare_com@1.0.0 | |
| concepts[field].local:field:ocean-atmospheric | mapping · figshare com | connector:figshare_com@1.0.0 | |
| concepts[field].local:field:social-science | mapping · figshare com | connector:figshare_com@1.0.0 | |
| concepts[method].local:method:longitudinal-study | enrichment · figshare com | keyword-concept-rules@1.0.0 | title+description (65%) |
| concepts[method].local:method:simulation | enrichment · figshare com | keyword-concept-rules@1.0.0 | title+description (75%) |
| description | source · figshare com | connector:figshare_com@1.0.0 | /metadata/dc/description |
| license | source · figshare com | connector:figshare_com@1.0.0 | /metadata/dc/rights |
| publication_date | source · figshare com | connector:figshare_com@1.0.0 | |
| title | source · figshare com | connector:figshare_com@1.0.0 | /metadata/dc/title |