Cost-effectiveness of community-based artificial intelligence screening for retinopathy of prematurity

Retinopathy of prematurity (ROP) is one of the leading causes of childhood blindness globally, but particularly in middle-income countries, where neonatal survival has improved faster than access to specialist ophthalmic care. Although telemedicine and artificial intelligence (AI)-assisted screening show promise for improving access to ROP screening, their economic value within decentralised health systems remains uncertain. To explore this issue, we evaluated the cost-effectiveness of community-based AI-assisted ROP screening in China compared with telemedicine and traditional bedside screening.

We developed a decision tree model to estimate lifetime health outcomes and societal costs for a hypothetical cohort of 100,000 preterm infants eligible for ROP screening. We compared three strategies: community-based AI-assisted screening, community-based telemedicine screening, and bedside screening at specialist healthcare facilities. Separate parameter sets were applied for urban and rural settings. Costs were assessed from a societal perspective.

Community-based AI-assisted screening was the most cost-effective strategy in both urban and rural settings. In the base-case analysis, telemedicine screening dominated bedside screening (meaning it was less costly and more effective), and AI-assisted screening further dominated telemedicine screening. AI-assisted screening incurred the lowest lifetime costs (USD 1,507 per infant in urban and USD 2,581 in rural areas) and the greatest health benefits (29.7138 quality-adjusted life-years (QALYs) in urban and 29.5835 QALYs in rural areas). Compared with bedside screening, AI-assisted screening was dominant, with incremental cost-effectiveness ratios of USD -7,307 per QALY gained in urban and USD -3,688 per QALY gained in rural settings. Findings were robust across scenario and sensitivity analyses, with AI-assisted screening ceasing to be cost-effective only at extremely low treatment-requiring ROP incidence (<0.7%) or low screening coverage (11%) in rural areas.

Community-based AI-assisted ROP screening is likely the most cost-effective strategy in China. By expanding access to early eye care while reducing societal costs, it may support equitable service delivery and offer a scalable model for other regions facing similar resource constraints.