ArticleJournal of global health2026
Cost-effectiveness of community-based artificial intelligence screening for retinopathy of prematurity.
Article in Journal of global health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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8 authors.
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Abstract
Background: 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. Methods: 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. Results: 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. Conclusions: 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.
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