Quantifying the Economic Value of AI-Assisted Diabetic Retinopathy Screening: A Meta-Analysis Using the Incremental Net Benefit Framework.

OBJECTIVE: To quantitatively synthesize the economic value of artificial intelligence (AI)-assisted screening for diabetic retinopathy (DR), a leading cause of preventable blindness. METHODS: We conducted a systematic review and meta-analysis of model-based economic evaluations comparing AI-assisted DR screening with traditional (human grader-based) screening or no screening. Eight databases were searched for studies published between January 1, 2015, and August 1, 2025. The incremental net monetary benefit (INMB) of AI-assisted screening versus each comparator was pooled using a random-effects model. Heterogeneity was explored through subgroup analysis by analytic perspective (healthcare system vs. societal). RESULTS: From 4,130 records, 14 studies were included in the systematic review, which 11 provided sufficient data for meta-analysis. Narrative synthesis indicated that most studies found AI-assisted screening to be cost-effective or cost-saving. Meta-analysis showed that AI-assisted screening was significantly more cost-effective than human grader-based screening, with a pooled INMB of $2,179.39 (95% CI: 1,165.13 to 3,193.65) per individual. Compared with no screening, AI-assisted screening yielded a pooled INMB of $3,606.10 (95% CI: 3,240.41 to 3,971.80) from a societal perspective. CONCLUSIONS: AI-assisted DR screening is a cost-effective strategy, particularly for expanding screening services in resource-limited settings. Its adoption in established programs should be informed by local factors such as ophthalmologist costs and program scale. Future evaluations should incorporate real-world evidence and adhere to standardized reporting guidelines.