10.1111/acn.v9999.9999 (CrossRef Listing of Deleted DOIs, 2000)
Purpose Diabetic retinopathy (DR) screening is essential to prevent vision loss, yet rising diabetes prevalence threatens to outpace ophthalmology capacity. Artificial intelligence (AI) systems can triage retinal images to reduce clinician workload, but economic evidence from high-income, tax-funded health systems remains limited. This study presents a cost-minimisation analysis (CMA) of clinician grading labour comparing deep-learning models (DLM)-assisted screening versus human-only DR screening. Methods This CMA evaluates two Danish healthcare settings differing in DR prevalence: In tertiary diabetes centres with DR grade 2-4 prevalence of 41% and in publicly contracted private ophthalmology practices with DR grade 2-4 prevalence of 6%. Costs were estimated from the healthcare-system perspective over a one-year horizon, including clinician time and wages for image assessment and verification. Sensitivity analyses explored variations in staff time, DR prevalence, DLM false-positive rate, and reimbursement effect. Results In the tertiary-centre setting (23 696 screens), total annual grading-labour costs were €282 192 for AI-assisted versus €421 425 for human-only screening (33% reduction). In private practice (84 494 screens), the AI-assisted pathway costs were €569 692 versus €1 502 698 (62% reduction). Savings were driven by reduced ophthalmologist time on cases the DLM classified as DR negative. Across all sensitivity analyses, the AI-assisted strategy remained cost-saving. Conclusion AI-assisted DR screening was cost-minimising in both high- and low-prevalence settings within Denmark's tax-funded health system, with greater savings in lower-prevalence populations. Tariff structures strongly influence payer impact, suggesting that reimbursement models must align with efficiency gains to realise health-system savings from AI triage in routine DR screening.