Application of artificial intelligence in osteoporosis: a review

Abstract

Osteoporosis is a systemic skeletal disease defined by decreased bone mass, deteriorated bone microarchitecture, and increased fracture susceptibility, all of which substantially increase the risks of functional disability and overall mortality. Given its complex pathophysiology, chronic progression, and frequently asymptomatic clinical presentation, the diagnosis and treatment of osteoporosis present multiple challenges. Artificial intelligence (AI) technologies integrate radiomics and multi-omics data to develop intelligent frameworks that map disease progression, identify molecular biomarkers, and stratify individual risk, offering new strategies for molecular-level support in precision diagnosis, preventive monitoring, and personalized intervention. Notably, AI-driven models for drug target prediction and therapeutic response evaluation have also been developed, offering mechanism-based strategies for novel drug discovery. This study outlines how AI advances osteoporosis management by applying technological breakthroughs in foundational and clinical research across key areas, including optimizing preventive monitoring, developing precision diagnostics, supporting personalized treatment decisions, and enabling molecular-targeted drug discovery, thereby offering a strong theoretical basis and practical pathways within precision and personalized medicine.

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