{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,22]],"date-time":"2026-08-22T14:25:14Z","timestamp":1787408714977,"version":"build-2736575974"},"reference-count":34,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,8,22]],"date-time":"2026-08-22T00:00:00Z","timestamp":1787356800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,8,22]],"date-time":"2026-08-22T00:00:00Z","timestamp":1787356800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Acta Ophthalmologica"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>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\u2010income, tax\u2010funded health systems remains limited. This study presents a cost\u2010minimisation analysis (CMA) of clinician grading labour comparing deep\u2010learning models (DLM)\u2010assisted screening versus human\u2010only DR screening.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This CMA evaluates two Danish healthcare settings differing in DR prevalence: In tertiary diabetes centres with DR grade 2\u20134 prevalence of 41% and in publicly contracted private ophthalmology practices with DR grade 2\u20134 prevalence of 6%. Costs were estimated from the healthcare\u2010system perspective over a one\u2010year horizon, including clinician time and wages for image assessment and verification. Sensitivity analyses explored variations in staff time, DR prevalence, DLM false\u2010positive rate, and reimbursement effect.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In the tertiary\u2010centre setting (23\u2009696 screens), total annual grading\u2010labour costs were \u20ac282\u2009192 for AI\u2010assisted versus \u20ac421\u2009425 for human\u2010only screening (33% reduction). In private practice (84\u2009494 screens), the AI\u2010assisted pathway costs were \u20ac569\u2009692 versus \u20ac1\u2009502\u2009698 (62% reduction). Savings were driven by reduced ophthalmologist time on cases the DLM classified as DR negative. Across all sensitivity analyses, the AI\u2010assisted strategy remained cost\u2010saving.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>AI\u2010assisted DR screening was cost\u2010minimising in both high\u2010 and low\u2010prevalence settings within Denmark's tax\u2010funded health system, with greater savings in lower\u2010prevalence populations. Tariff structures strongly influence payer impact, suggesting that reimbursement models must align with efficiency gains to realise health\u2010system savings from AI triage in routine DR screening.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/aos.70225","type":"journal-article","created":{"date-parts":[[2026,8,22]],"date-time":"2026-08-22T13:55:35Z","timestamp":1787406935000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Grading\u2010labour cost analysis of\n                    <scp>AI<\/scp>\n                    \u2010assisted versus human\u2010only diabetic retinopathy screening in two Danish healthcare settings"],"prefix":"10.1111","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3693-8394","authenticated-orcid":false,"given":"Lars","family":"Gr\u00f8nlykke","sequence":"first","affiliation":[{"name":"Department of Cardiothoracic Anaesthesiology Copenhagen University Hospital, Rigshospitalet  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