Economic Evaluation of Artificial Intelligence Systems Versus Manual Screening for Diabetic Retinopathy in the United States

Abstract

Background: The objective of this economic modeling study was to compare the cost effectiveness of fully automated retinal image screening (FARIS) to the current practice of universal ophthalmologist referral for diabetic retinopathy in the United States (US) health care system. Methods: A Markov decision-analytic model was used to compare the automated versus manual screening and management pathway for diabetic patients with unknown retinopathy status. Costs (in 2021 US dollars), quality-adjusted life year (QALY) gains, and incremental cost-effectiveness ratios were calculated. Sensitivity analysis was performed against a $50,000/QALY willingness-to-pay threshold. Results: FARIS was the dominant screening strategy, demonstrating cost savings of 18.8% at 5 years with equivalent net QALY gains to manual screening. Cost-effectiveness status was dependent on FARIS detection specificity, with a threshold value of 54.8%. Conclusion: Artificial intelligence-based screening represents an economically advantageous screening modality for diabetic retinopathy in the US, offering equivalent long-term utility with significant potential cost savings. [ Ophthalmic Surg Lasers Imaging Retina 2023;54:272–280.]