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Quantifying the Economic Value of Artificial Intelligence-Assisted Diabetic Retinopathy Screening: A Meta-Analysis
Chen Wei1, Liujin Zhang1, Chen Chen1
1Center for Health Policy and Health Economics, National Infrastructures for Translational Medicine, Institute of Clinical Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China.
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. A total of 8 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 4130 records, 14 studies were included in the systematic review, of 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 $2179.39 (95% confidence interval [CI]: 1165.13 to 3193.65) per individual. Compared with no screening, AI-assisted screening yielded a pooled INMB of $3606.10 (95% CI: 3240.41 to 3971.80) 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.
Trial Registration:
CRD42025104131.
