AI framework for multidisease detection via retinal imaging

The rising burden of endocrine and metabolic diseases demands scalable and accessible screening tools. Here we developed Reti-Pioneer, a multitask retinal imaging framework that integrates quality-aware modules with pre-trained foundation models for efficient, multidisease detection. In general, the framework was developed using 107,730 color fundus photographs from both community-based and hospital-based cohorts and achieved area under the receiver operating characteristic curve values on internal test data of 0.833 (95% confidence interval 0.810–0.856) for type 2 diabetes mellitus, 0.832 (0.799–0.866) for gout, 0.787 (0.742–0.833) for osteoporosis, 0.740 (0.726–0.755) for hypertension, 0.736 (0.721–0.751) for hyperlipidemia and 0.699 (0.667–0.730) for thyroid disease. The framework generalized well to six external cohorts from both resource-limited and high-resource settings, and showed biological interpretability via plasma proteomic correlations. In a primary care silent trial, it completed screening in 30.6 ± 6.0 s per case, notably faster than standard laboratory workflows. A subsequent clinical pilot for type 2 diabetes mellitus yielded an area under the receiver operating characteristic curve of 0.776 (0.710–0.842) and negative predictive value of 0.966 (0.946–0.983), surpassing the Finnish Diabetes Risk Score, with high acceptance from clinicians and patients. Overall, Reti-Pioneer could provide a translatable, low-cost pathway from oculomics to actionable clinical screening. Reti-Pioneer, a retinal imaging artificial intelligence framework, identified diverse systemic diseases and demonstrated feasibility in a primary care silent trial, offering a pathway toward scalable clinical evaluation.