Effectiveness of screening modalities for early detection of diabetic retinopathy: a systematic review and meta-analysis of tele-ophthalmology, AI-based tools, and conventional methods

Background: Diabetic retinopathy (DR) causes severe vision impairment that requires early screening methods for effective detection. The combination of Artificial Intelligence (AI) and tele-ophthalmology technology provides an effective solution that enhances both DR detection rates and patient access to care. Objective: The study aims to assess how well AI-based telemedicine smartphone conventional and population-based screening methods detect diabetic retinopathy in terms of effectiveness, accuracy, and real-world performance. Methodology: , and test for publication bias with funnel plots and Egger's test. Results: = 0.019). All methods achieved statistically significant progress in DR detection research. Conclusion: AI-based screening, including deep learning algorithms, automated grading, and tele-ophthalmology, shows high diagnostic accuracy and consistent effectiveness. The implementation of smartphone- and population-based approaches is feasible, yet their outcomes vary, necessitating validation through context-specific assessments.