{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T06:06:50Z","timestamp":1792822010436,"version":"4.5.0"},"reference-count":27,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T00:00:00Z","timestamp":1765497600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,1,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>South Korea, now a \u201csuper-aged\u201d society, faces a rising burden of fragility fractures, yet underdiagnosis remains a major barrier, with limited DXA access restricting early detection. Artificial intelligence (AI) applied to routine chest radiographs enables opportunistic screening. In March 2025, Osteo Signal, a deep learning model developed in the South Korean population, received regulatory approval for use in both men and women. Unlike prior evaluations in women only, this study assessed its cost-effectiveness in adults aged \u226550\u00a0yr of both sexes. A model estimated the cost per quality-adjusted life year (QALY) gained (2025 Korean Won, KRW) from opportunistic AI-assisted chest radiograph screening vs no screening. Model inputs included osteoporosis prevalence (fixed prevalence: 37% in women and 7.5% in men and age-specific prevalence), diagnostic performance of Osteo Signal, and realistic probabilities of DXA confirmation, treatment initiation, and medication persistence. Patients were assumed to receive alendronate or denosumab. Analyses were performed in the overall population and by sex. In the fixed-prevalence scenario, screening improved outcomes, preventing 46 fractures, gaining 21 life years and 36 QALYs per 10\u2009000 adults, while increasing treatment expenditures. The incremental cost-effectiveness ratio (ICER) was KRW 12\u2009096\u2009960 (~USD 8650) per QALY in the overall population, below South Korea\u2019s willingness-to-pay threshold (KRW 30 million). Subgroup ICERs were KRW 8\u2009910\u2009449 for women and KRW 44\u2009746\u2009862 for men. In the age-specific prevalence scenario, the ICERs were KRW 7\u2009473\u2009124 for the overall population, KRW 4\u2009941\u2009078 for women, and KRW 30\u2009006\u2009944 for men. Artificial intelligence-enhanced chest radiograph screening is cost-effective for South Korean adults aged \u226550\u00a0yr when evaluated across both men and women. While sex-specific differences exist, the combined analysis highlights meaningful population-level value, supporting adoption to reduce the national burden of osteoporosis and fractures.<\/jats:p>","DOI":"10.1093\/jbmrpl\/ziaf187","type":"journal-article","created":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T13:10:19Z","timestamp":1765545019000},"source":"Crossref","is-referenced-by-count":6,"title":["Evaluating the cost-effectiveness of artificial intelligence-enhanced osteoporosis screening in men and women using routine chest radiographs in South Korea"],"prefix":"10.1093","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4274-9258","authenticated-orcid":false,"given":"Mickael","family":"Hiligsmann","sequence":"first","affiliation":[{"name":"Department of Health Services Research, CAPHRI Care and Public Health Research Institute, Maastricht University , Maastricht , 6200 MD,","place":["The Netherlands"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8791-0909","authenticated-orcid":false,"given":"Sung Hye","family":"Kong","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, Seoul National University Bundang Hospital , Seongnam, Gyeonggi-do 13620,","place":["Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Majed","family":"Alokail","sequence":"additional","affiliation":[{"name":"Protein Research Chair, Biochemistry Department, College of Science, King Saud University , Riyadh 11453 ,","place":["Saudi Arabia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mi-Young","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, Madeulsoo Internal Medicine Clinic , Seoul 01678 ,","place":["Republic of Korea"]},{"name":"Medical Affairs, Promedius Inc. , Seoul 05510 ,","place":["Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jean-Yves","family":"Reginster","sequence":"additional","affiliation":[{"name":"Protein Research Chair, Biochemistry Department, College of Science, King Saud University , Riyadh 11453 ,","place":["Saudi Arabia"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2025,12,12]]},"reference":[{"issue":"4","key":"2026012703122331800_ref1","doi-asserted-by":"publisher","first-page":"3000605241244754","DOI":"10.1177\/03000605241244754","article-title":"Deep learning in the radiologic diagnosis of osteoporosis: a literature review","volume":"52","author":"He","year":"2024","journal-title":"J Int Med Res"},{"issue":"2","key":"2026012703122331800_ref2","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1002\/jbmr.4477","article-title":"Opportunistic osteoporosis screening using chest radiographs with deep learning: development and external validation with a 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