摘要
Objective:Diabetic retinopathy(DR)is a top leading cause of blindness worldwide,requiring early detection for timely intervention.Artificial intelligence(AI)has emerged as a promising tool to improve DR screening efficiency,accessibility,and cost-effectiveness.This study conducted a systematic review of literature and meta-analysis on the economic outcomes of AI-based DR screening.Methods:A systematic review of studies published before September 2024 was conducted throughout PubMed,Scopus,Embase,the Cochrane Library,the National Health Service Economic Evaluation Database,and the Cost-Effectiveness Analysis Registry.Eligible studies were included if they were(1)conducted among type 1 diabetes mellitus or type 2 diabetes mellitus adult diabetic population;(2)studies compared AI-based DR screening strategy to non-AI screening;and(3)performed a cost-effectiveness analysis.Meta-analysis was applied to pool incremental net benefit(INB)across studies stratified by country income and study perspective using a random-effects model.Statistical heterogeneity among studies was assessed using the I2 statistic,Cochrane Q statistics,and meta regression.Results:Nine studies were included in the analysis.From a healthcare system/payer perspective,AI-based DR screening was significantly cost-effective compared to non-AI-based screening,with a pooled INB of 615.77(95%confidence interval[CI]:558.27-673.27).Subgroup analysis showed robust cost-effectiveness of AI-based DR screening in high-income countries(INB=613.62,95%CI:556.06-671.18)and upper-/lower-middle income countries(INB=1,739.97,95%CI:423.13-3,056.82)with low heterogeneity.From a societal perspective,AI-based DR screening was generally cost-effective(INB=5,102.33,95%CI:-815.47-11,020.13),though the result lacked statistical significance and showed high heterogeneity.Conclusions:AI-based DR screening is generally cost-effective from a healthcare system perspective,particularly in high-income countries.Heterogeneity in cost-effectiveness across different perspectives highlights the importance of context-specific evaluations,to accurately evaluate the potential of AI-based DR screening in reducing global healthcare disparities.
基金
supported by the Global STEM Professorship Scheme(P0046113)
Henry G.Leong Endowed Professorship in Elderly Vision Health.
