Artificial intelligence in diabetic retinopathy: from automated screening to risk-stratified care.

Diabetic retinopathy (DR) is a leading cause of preventable blindness worldwide. The rising prevalence of diabetes has strained conventional screening pathways, which are labor-intensive and depend on specialist image interpretation, limiting access, particularly in resource-limited settings. Artificial intelligence (AI) offers a promising approach to improve the scalability, consistency, and reach of DR detection and management. This review examines AI applications in DR, spanning automated screening, grading, lesion segmentation, multimodal imaging integration, personalized risk prediction, and AI-driven metabolomics, while also addressing translational challenges and future directions. We conducted a narrative review using major biomedical databases and targeted searches of peer-reviewed literature, prioritizing recent systematic reviews, meta-analyses, clinical trials, implementation studies, and seminal technical reports. Current evidence shows that AI systems can detect referable DR with diagnostic performance approaching or exceeding expert human graders. Deep learning methods have improved lesion detection, severity grading, and risk assessment. Multimodal approaches integrating fundus photography with OCT, OCTA, and clinical data may enhance disease characterization and risk stratification, but most evidence remains developmental or early translational. Likewise, AI-guided personalized follow-up, treatment modeling, and metabolomics-based biomarker discovery are promising but less clinically validated than screening and grading applications. Real-world studies support implementation feasibility, with gains in screening uptake and workflow efficiency. Key barriers to broader adoption include external validation, heterogeneous reference standards, management of ungradable images, domain shift across devices and populations, workflow integration, and regulatory and ethical considerations. AI is poised to strengthen scalable DR care, but personalized applications require further validation before routine clinical use.