From pixels to precision: Artificial intelligence in diabetic eye disease screening and management

Artificial intelligence (AI) is transforming the landscape of diabetic care, bridging the gap between early detection and personalized disease management. In ophthalmology, AI-driven algorithms have demonstrated remarkable accuracy in identifying diabetic retinopathy and diabetic macular edema from retinal fundus and optical coherence tomography images, rivaling expert graders while offering scalable, cost-effective solutions for population-level screening. Beyond image analysis, AI is emerging as a powerful decision-support tool that integrates systemic data, glycemic control metrics, and imaging biomarkers to predict disease progression and treatment response. This narrative opinion review explores how AI can reshape diabetic patient management through predictive analytics, remote monitoring, and automated triage systems, enabling timely referrals and individualized follow-up. We also discuss the ethical, regulatory, and practical barriers to clinical translation, emphasizing the need for explainable algorithms, real-world validation, and interdisciplinary collaboration. By shifting from reactive treatment to proactive prediction, AI promises to redefine the continuum of diabetic eye care - from screening to precision-guided management - ultimately reducing blindness and healthcare burden worldwide.