Cost-effectiveness of Artificial Intelligence- Enabled Screening for Diabetic Retinopathy: A Systematic Review

Early detection of diabetic retinopathy (DR) is critical for preventing vision impairment. Therefore, this review was conducted to evaluate the cost-effectiveness of artificial intelligence-enabled screening for DR. PubMed, Google scholar, Web of Science, and the Health Technology Assessment database were searched for relevant articles published up to January 2025. Twelve studies were included in the systematic review. Study quality was assessed using the JBI Checklist for Economic Evaluations. We found that AI-enabled DR screening was cost-effective across diverse settings. The main factors affecting its cost-effectiveness were found to be labor costs for manual DR screening, screening accuracy of AI systems, and patient compliance for referrals. Evidence strongly supports the implementation of AI-enabled screening for DR. Overall, AI-enabled DR screening represents a cost-effective and scalable strategy that can transform diabetic eye care globally and support equitable access in low- and middle-income countries.