Diabetic Retinopathy (DR) is a leading cause of vision loss worldwide. Early detection remains the cornerstone for preventing visual disability. Artificial Intelligence (AI) has recently emerged as a transformative tool in DR screening.<br /><br />Deep learning algorithms, particularly Convolutional Neural Networks (CNNs), analyze fundus photographs with remarkable accuracy, identifying microaneurysms, hemorrhages, and exudates. Several AI systems have achieved sensitivity levels >95% and specificity >90%.<br />AI-based screening allows large-scale population coverage, especially in underserved regions with limited access to ophthalmologists. Moreover, automated triage systems reduce hospital workload by prioritizing high-risk patients.<br />Despite these benefits, limitations include reliance on high-quality images and difficulty predicting rapid disease progression.<br /><br />Future Perspectives<br />• AI integration with electronic health records for personalized care.<br />• Multi-disease detection (DR, AMD, glaucoma) from a single retinal image.<br />• Use of biochemical and vitreous biomarkers alongside AI for predictive modeling.<br /><br />Conclusion<br />AI-enabled DR screening represents a paradigm shift in ophthalmology, enabling scalable, cost-effective, and accurate detection strategies that could substantially reduce global blindness.<br />