Trial reportNature medicine2026
AI-based clinician decision support system for diagnosis of inherited retinal diseases: a multicenter, randomized trial.
Trial report in Nature medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06839170 (A Multi-stage Evaluation Foundation Model for Inherited Retinal Diseases Care Needs), which is not on this map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
A Multi-stage Evaluation Foundation Model for Inherited Retinal Diseases Care Needs: A Randomized Control Trial
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Authors and funding
37 authors.
Funding
Abstract
The accurate and timely diagnosis of inherited retinal diseases (IRDs) represents an unmet clinical need in ophthalmology, as the current pathways rely on resource-intensive phenotyping, multidisciplinary expertise and genetic testing. Here we developed Retina4IRD, an artificial intelligence (AI)-based clinician decision support system (CDSS) that predicts 17 genotype categories from retina images. Retina4IRD uses a Vision Transformer model pretrained with RETFound. We then trained and validated Retina4IRD using multimodal data with color fundus photographs and optical coherence tomography scans from 1,843 genetically confirmed patients (3,376 eyes) across China, South Korea and Poland. The top-5 prediction accuracy was 0.904 (95% confidence interval (CI): 0.896-0.912) and 0.856 (95% CI: 0.850-0.863) for internal and external validation, respectively. We conducted a randomized controlled trial with 300 participants with suspected IRD randomized 1:1 to either Retina4IRD-assisted specialist arm or specialist-only arm. Of these, 295 participants (median age 33 years, 114 (38.6%) females) with available next-generation sequencing reports were included in the final analysis. The primary outcome was met: top-5 genetic accuracy was significantly higher in the Retina4IRD-assisted specialist arm versus the specialist-only arm (88.5% versus 67.3%, P < 0.001). For secondary endpoints, top-1 to top-4 accuracies all favored the Retina4IRD-assisted specialist arm, with top-1 accuracy of 37.8% versus 22.4% and top-4 accuracy of 81.8% versus 53.1%, respectively. Post hoc analyses demonstrated that, with Retina4IRD assistance, clinicians made better management decisions, and the composite downstream management score indicated significantly higher scores relative to the control group (37.7 versus 28.5, P < 0.001). Our study shows that Retina4IRD is a CDSS tool prior to genetic testing and aligns with clinical workflow for patients with suspected IRDs. ClinicalTrials.gov identifier: NCT06839170 .
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