Artificial intelligence in diabetic retinopathy: From deep learning to foundation models, clinical translation, and real-world deployment.

Diabetic retinopathy (DR) remains a leading cause of preventable vision impairment worldwide, with its burden increasing in parallel with the global diabetes pandemic. As a progressive microvascular disorder with neurovascular unit involvement, DR is driven by retinal ischemia, inflammation, and pathological neovascularization, while diabetic macular edema (DME) continues to represent a principal cause of vision loss. This structured narrative review provides a critical synthesis of advances in diabetic retinopathy diagnosis, retinal imaging, treatment strategies, and artificial intelligence (AI)-enabled screening published from January 2020 through March 2026, while selectively incorporating earlier landmark studies to provide clinical and methodological context. We summarize contemporary screening pathways and emerging retinal imaging modalities, including ultra-widefield photography (up to ~200° field of view), optical coherence tomography (OCT), and OCT angiography, examining how complementary structural and vascular biomarkers are reshaping disease severity assessment and DME characterization. We then review the evolution of AI methodologies, highlighting a transition from task-specific convolutional neural network pipelines toward transformer-based and self-supervised representation learning, including retina-specific foundation models designed to reduce annotation burden and improve robustness and generalizability under domain shift. Beyond algorithmic performance on curated benchmarks, we emphasize key translational requirements for clinical adoption, including model calibration, uncertainty-aware triage, image quality control, and external validation across imaging devices and diverse populations. Recent prospective and implementation studies across diverse healthcare systems demonstrate that the real-world effectiveness of AI-enabled DR screening depends not only on diagnostic accuracy but also on image gradability, clinically appropriate operating thresholds, uncertainty management, external validation, referral capacity, and integration with established care pathways. Evidence from the Asia-Pacific region provides important examples of these implementation challenges in heterogeneous and resource-constrained screening environments. Specific attention is given to the identification and mitigation strategies for fundus pigmentation-related performance disparities and the governance of autonomous versus hybrid human-AI screening models within evolving regulatory frameworks. Finally, we examine emerging work in oculomics for systemic risk stratification and propose a pragmatic clinical readiness framework centered on validation hierarchy, safety monitoring, and workflow integration. Particular emphasis is placed on the translational readiness of foundation models, multimodal retinal intelligence, and emerging technologies for large-scale screening, therapeutic decision-making, and longitudinal disease monitoring.