{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T17:33:11Z","timestamp":1790875991960,"version":"4.1.0"},"reference-count":0,"publisher":"Association of Basic Medical Sciences of FBIH","issue":"12","license":[{"start":{"date-parts":[[2025,7,7]],"date-time":"2025-07-07T00:00:00Z","timestamp":1751846400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biomol Biomed"],"abstract":"<jats:p>Body Mass Index (BMI) has long been used as a standard measure for assessing population-level health risks, but its clinical adequacy has increasingly been called into question. This opinion paper challenges the clinical adequacy of BMI and presents AI-enhanced CT body composition analysis as a superior alternative for individualized risk assessment. While BMI serves population-level screening, its inability to differentiate between tissue types leads to critical misclassifications, particularly for sarcopenic obesity. AI-powered analysis of CT imaging at the L3 vertebra level provides precise quantification of skeletal muscle index, visceral, and subcutaneous adipose tissues -metrics that consistently outperform BMI in predicting outcomes across oncology, cardiology, and critical care. Recent technological advances have transformed this approach: the \"opportunistic\" use of existing clinical CT scans eliminates radiation concerns, while AI automation has reduced analysis time from 15-20 minutes to mere seconds. These innovations effectively address previous implementation barriers and enable practical clinical application with minimal resource demands, creating opportunities for targeted interventions and personalized care pathways.<\/jats:p>","DOI":"10.17305\/bb.2025.12774","type":"journal-article","created":{"date-parts":[[2025,7,7]],"date-time":"2025-07-07T18:00:24Z","timestamp":1751911224000},"page":"2586-2593","source":"Crossref","is-referenced-by-count":7,"title":["Beyond BMI: An opinion on the clinical value of AI-powered CT body composition analysis"],"prefix":"10.17305","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6238-2276","authenticated-orcid":false,"given":"Matej","family":"Pekar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marek","family":"Kantor","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jakub","family":"Balusik","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2514-2059","authenticated-orcid":false,"given":"Jan","family":"Hecko","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-9471-6850","authenticated-orcid":false,"given":"Piotr","family":"Branny","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"6922","published-online":{"date-parts":[[2025,7,7]]},"container-title":["Biomolecules and Biomedicine"],"original-title":[],"link":[{"URL":"https:\/\/bjbms.org\/ojs\/index.php\/bjbms\/article\/download\/12774\/3955","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/bjbms.org\/ojs\/index.php\/bjbms\/article\/download\/12774\/3955","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T23:04:06Z","timestamp":1761779046000},"score":1,"resource":{"primary":{"URL":"https:\/\/bjbms.org\/ojs\/index.php\/bjbms\/article\/view\/12774"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,7]]},"references-count":0,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2025,9,19]]}},"URL":"https:\/\/doi.org\/10.17305\/bb.2025.12774","relation":{},"ISSN":["2831-090X","2831-0896"],"issn-type":[{"value":"2831-090X","type":"electronic"},{"value":"2831-0896","type":"print"}],"subject":[],"published":{"date-parts":[[2025,7,7]]}}}