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The potential role for artificial intelligence in fracture risk prediction

Authors
 Namki Hong  ;  Danielle E Whittier  ;  Claus-C Glüer  ;  William D Leslie 
Citation
 LANCET DIABETES & ENDOCRINOLOGY, Vol.12(8) : 596-600, 2024-08 
Journal Title
LANCET DIABETES & ENDOCRINOLOGY
ISSN
 2213-8587 
Issue Date
2024-08
MeSH
Artificial Intelligence* ; Humans ; Machine Learning ; Osteoporosis / therapy ; Osteoporotic Fractures* / epidemiology ; Osteoporotic Fractures* / prevention & control ; Risk Assessment / methods ; Risk Factors
Abstract
Osteoporotic fractures are a major health challenge in older adults. Despite the availability of safe and effective therapies for osteoporosis, these therapies are underused in individuals at high risk for fracture, calling for better case-finding and fracture risk assessment strategies. Artificial intelligence (AI) and machine learning (ML) hold promise for enhancing identification of individuals at high risk for fracture by distilling useful features from high-dimensional data derived from medical records, imaging, and wearable devices. AI–ML could enable automated opportunistic screening for vertebral fractures and osteoporosis, home-based monitoring and intervention targeting lifestyle factors, and integration of multimodal features to leverage fracture prediction, ultimately aiding improved fracture risk assessment and individualised treatment. Optimism must be balanced with consideration for the explainability of AI–ML models, biases (including information inequity in numerically under-represented populations), model limitations, and net clinical benefit and workload impact. Clinical integration of AI–ML algorithms has the potential to transform osteoporosis management, offering a more personalised approach to reduce the burden of osteoporotic fractures.
Full Text
https://www.clinicalkey.com/#!/content/playContent/1-s2.0-S2213858724001530
DOI
10.1016/S2213-8587(24)00153-0
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
Yonsei Authors
Hong, Nam Ki(홍남기) ORCID logo https://orcid.org/0000-0002-8246-1956
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/200828
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