{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T16:07:20Z","timestamp":1791216440223,"version":"4.1.0"},"reference-count":43,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,12,23]],"date-time":"2025-12-23T00:00:00Z","timestamp":1766448000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Medicina"],"abstract":"<jats:p>Background and Objectives: Osteoporosis is a prevalent skeletal disorder characterized by decreased bone mass and compromised bone microarchitecture, leading to an elevated risk of fractures and significant morbidity, particularly among aging populations. Early diagnosis and personalized management are critical to reducing fracture incidence and associated healthcare burdens. Recent advances in artificial intelligence (AI) and machine learning (ML) have led to potential improvements in enhancing osteoporosis care by enabling accurate diagnostic imaging analysis, robust fracture risk prediction, and personalized therapeutic strategies. Materials and Methods: We performed a narrative review to summarize and critically evaluate the current literature on AI and ML applications in osteoporosis diagnosis and management. We searched relevant literature from inception to January 2025 to provide a comprehensive perspective, focusing on key themes, methodological approaches, and clinical implications. Results: Deep learning models, especially convolutional neural networks, facilitate rapid and accurate bone mineral density assessment from routine radiographs, expanding screening capabilities beyond conventional dual-energy X-ray absorptiometry (DXA). Machine learning algorithms harness clinical and demographic data to generate fracture risk models that often outperform traditional tools, enabling timely identification of high-risk individuals. Furthermore, AI-driven analyses of historical treatment responses coupled with real-time monitoring through wearable technologies and mobile applications allow for personalized therapeutic optimization and enhance patient engagement. Despite these promising advances, challenges remain regarding ethical considerations, data privacy, legal liability, incomplete model validation, lack of standardization, and the need for critical appraisal of real-world clinical efficacy for widespread clinical adoption. Conclusions: This narrative review indicates that AI and ML hold significant promise to revolutionize osteoporosis management by enabling early detection, precise risk stratification, and tailored interventions. However, the current evidence is heterogeneous, often lacking robust external validation and quantitative synthesis. Critical gaps include insufficient evaluation of model robustness across diverse populations, discussion of negative or conflicting results, and a comprehensive assessment of the limitations inherent in current AI evidence. Strategic efforts to validate, regulate, and critically integrate these technologies into routine clinical workflows are essential to realize their full potential and address the growing burden of osteoporosis worldwide.<\/jats:p>","DOI":"10.3390\/medicina62010027","type":"journal-article","created":{"date-parts":[[2025,12,23]],"date-time":"2025-12-23T10:57:18Z","timestamp":1766487438000},"page":"27","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Artificial Intelligence and Machine Learning in the Diagnosis and Management of Osteoporosis: A Comprehensive Review"],"prefix":"10.3390","volume":"62","author":[{"given":"Alessandro","family":"Conforti","sequence":"first","affiliation":[{"name":"ASL Roma 4, 00053 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marco","family":"Ruggiero","sequence":"additional","affiliation":[{"name":"Spinal Unit, CTO Hospital, ASL Roma 2, 00159 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linda","family":"Lucchetti","sequence":"additional","affiliation":[{"name":"Hospital Pharmacy Unit, ASL Roma 4, 00053 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Valerio","family":"Cipolloni","sequence":"additional","affiliation":[{"name":"Department of Orthopaedics, A.O.U. \u201cVanvitelli\u201d University Hospital, \u201cLuigi Vanvitelli\u201d University, Via del Sole 10, 80138 Naples, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francesco Demostene","family":"Galati","sequence":"additional","affiliation":[{"name":"Fondazione Policlinico Universitario Campus Bio Medico, 00128 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martina","family":"Gentile","sequence":"additional","affiliation":[{"name":"Ambulatorio Reumatologia Montefiascone\u2014Bagnoregio, ASL Viterbo, 01100 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4383-0314","authenticated-orcid":false,"given":"Alberto","family":"Lo Gullo","sequence":"additional","affiliation":[{"name":"UOSD Reumatologia, A.O. Papardo, 98158 Messina, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Van Oostwaard, M. (2018). Osteoporosis and the Nature of Fragility Fracture: An Overview. Perspectives in Nursing Management and Care for Older Adults, Springer.","DOI":"10.1007\/978-3-319-76681-2_1"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1243","DOI":"10.1093\/gerona\/glt093","article-title":"Public health impact of osteoporosis","volume":"68","author":"Cauley","year":"2013","journal-title":"J. Gerontol. Ser. A"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"171","DOI":"10.5312\/wjo.v7.i3.171","article-title":"Major osteoporotic fragility fractures: Risk factor updates and societal impact","volume":"7","author":"Pisani","year":"2016","journal-title":"World J. 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