This narrative review demonstrates advancements in osteoporosis screening using machine learning, suggesting future improvements in diagnosis and outcomes.
Machine learning has the potential to address limitations of traditional osteoporosis screening through advanced data processing and pattern recognition capabilities. This review provides a critical analysis of ML applications in opportunistic screening, precision risk assessment, and quantitative bone quality analysis, while addressing current implementation challenges and future research directions to enhance early diagnosis, reduce fracture risk, and improve patient outcomes. As a narrative synthesis, this review highlights strategic directions and translational challenges rather than providing a systematic appraisal of evidence. Machine learning offers promising approaches to address the limitations of traditional osteoporosis screening.
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Shen et al. (2026) studied this question.
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