{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T22:35:25Z","timestamp":1783550125555,"version":"3.55.0"},"reference-count":41,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T00:00:00Z","timestamp":1763510400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:p>Osteoporosis (OP) is a systemic bone metabolic disorder characterized by a decrease in bone mineral density (BMD) and damage to the trabecular bone microarchitecture. With the increasing global aging population, the incidence of OP has been rising annually, particularly among elderly women, making it a significant public health issue. Traditional diagnostic methods such as dual-energy X-ray absorptiometry (DXA), quantitative computed tomography (QCT), and magnetic resonance imaging (MRI) are effective, but they also have certain limitations. Artificial intelligence (AI) technology is playing an increasingly important role in the management of osteoporosis. Through machine learning (ML), image processing, and data analysis, AI can accurately assess bone density, fracture risk, and other factors, improving the early diagnosis rate of OP and providing strong decision support for clinicians to optimize treatment plans and enhance treatment outcomes. However, it also faces challenges such as AI model interpretability, insufficient diversity in training data, lack of clinical validation, and issues related to privacy protection and ethics. Addressing these problems is crucial for promoting the widespread application of AI technology in this field. As technology continues to advance, AI will become an indispensable part of OP research and clinical applications, driving the development of personalized treatment and precision medicine.<\/jats:p>","DOI":"10.3389\/frai.2025.1699762","type":"journal-article","created":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T06:36:34Z","timestamp":1763534194000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["The application progress of artificial intelligence in osteoporosis diagnosis"],"prefix":"10.3389","volume":"8","author":[{"given":"Wenwei","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongxu","family":"Tai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siwen","family":"Kang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Keda","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,11,19]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"1117","DOI":"10.1016\/j.mcna.2021.05.016","article-title":"Update on osteoporosis screening and management","volume":"105","author":"Anam","year":"2021","journal-title":"Med. 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Soc."},{"key":"ref11","doi-asserted-by":"publisher","first-page":"1831","DOI":"10.1007\/s00330-020-07312-8","article-title":"Opportunistic osteoporosis screening in multi-detector CT images using deep convolutional neural networks","volume":"31","author":"Fang","year":"2021","journal-title":"Eur. Radiol."},{"key":"ref12","doi-asserted-by":"publisher","first-page":"18330","DOI":"10.1038\/s41598-022-23184-y","article-title":"Artificial intelligence used to diagnose osteoporosis from risk factors in clinical data and proposing sports protocols","volume":"12","author":"Fasihi","year":"2022","journal-title":"Sci. Rep."},{"key":"ref13","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1016\/s0887-2171(02)90012-0","article-title":"How do radiographic techniques affect image quality and patient doses in CT?","volume":"23","author":"Huda","year":"2002","journal-title":"Semin. Ultrasound CT MR"},{"key":"ref14","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1007\/s11657-020-00871-9","article-title":"SCOPE 2021: a new scorecard for osteoporosis in Europe","volume":"16","author":"Kanis","year":"2021","journal-title":"Arch. 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Med."},{"key":"ref18","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1007\/s40134-013-0031-y","article-title":"Emerging research on bone health using high-resolution CT and MRI","volume":"2","author":"Liebl","year":"2013","journal-title":"Curr. Radiol. Rep."},{"key":"ref19","doi-asserted-by":"publisher","first-page":"e231937","DOI":"10.1148\/radiol.231937","article-title":"Osteoporotic precise screening using chest radiography and artificial neural network: the OPSCAN randomized controlled trial","volume":"311","author":"Lin","year":"2024","journal-title":"Radiology"},{"key":"ref20","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1055\/s-0039-1677903","article-title":"Artificial intelligence in clinical decision support: challenges for evaluating AI and practical implications","volume":"28","author":"Magrabi","year":"2019","journal-title":"Yearb. Med. 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