Real-World Performance of Artificial Intelligence in Diabetic Retinopathy Screening: A Systematic Review

Diabetic retinopathy (DR) remains a leading cause of preventable blindness worldwide, and timely screening is essential for early detection and intervention. Artificial intelligence (AI), particularly deep learning, has emerged as a promising tool for automated diabetic retinopathy screening. This systematic review evaluates the diagnostic performance and real-world applicability of AI-based systems across diverse clinical settings. A systematic search of PubMed, Excerpta Medica database (Embase), and the Cochrane Library was conducted, supplemented by screening of Google Scholar, with study selection performed in accordance with PRISMA 2020 guidelines. Studies were included if they assessed AI systems for diabetic retinopathy detection using fundus-based retinal imaging and reported diagnostic accuracy outcomes. A total of 30 studies published between 2016 and 2025 were included. Across studies, AI systems demonstrated consistently high diagnostic performance, with most reporting sensitivities above 85% and specificities above 80%. Large-scale and real-world studies confirmed the feasibility of implementing AI in national and community screening programmes. Additionally, smartphone-based and handheld imaging systems demonstrated promising potential for expanding screening access in resource-limited settings. Despite these encouraging findings, variability between AI systems and study designs highlights the need for external validation and standardisation prior to widespread clinical adoption. AI has significant potential to enhance screening efficiency and accessibility, but further research is required to evaluate long-term clinical outcomes and integration into healthcare systems.