Cost-Effectiveness of Artificial Intelligence Assisted Screening for Diabetic Retinopathy: A Markov Model

Diabetic retinopathy (DR) is a leading cause of vision loss in individuals with diabetes, highlighting the need for timely screening. In Taiwan, limited ophthalmologic resources, especially in underserved areas, constrain screening coverage. This study evaluated the cost-effectiveness of artificial intelligence (AI)-assisted DR screening as an alternative strategy for early detection and improved resource allocation.

A Markov decision-tree model was constructed from the healthcare payer's perspective, using transition probabilities, costs, and quality-adjusted life years (QALYs) derived from domestic and international data. The model applied a 1-year cycle length, a 10-year time horizon, a 3% annual discount rate, and 10,000 Monte Carlo simulations. Incremental cost-effectiveness ratios (ICERs) were calculated for AI-assisted versus ophthalmologist-based screening, with probabilistic and one-way sensitivity analyses conducted to evaluate robustness. Statistical analyses were conducted using SPSS version 23.0, while cost-effectiveness analyses were performed using TreeAge Pro Healthcare 2021.

AI-assisted screening incurred higher costs ($10,077.34) than ophthalmologist-based screening ($8282.06) but provided greater health benefits (7.60 vs. 6.34 QALYs). The ICER was $1429.19/QALYs, well below willingness-to-pay threshold ($33,983, 2024 Taiwan per capita gross domestic product), demonstrating high cost-effectiveness.

AI-assisted DR screening is a cost-effective approach that may enhance access, especially in regions with limited specialist availability. By enabling earlier detection and reducing reliance on ophthalmologists, AI-based screening has the potential to improve both efficiency and equity in healthcare delivery. These findings support its integration into national screening programs and emphasise the importance of local data in informing policy decisions.