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.

中文翻译：

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人工智能在骨折风险预测中的潜在作用


骨质疏松性骨折是老年人面临的主要健康挑战。尽管有安全有效的骨质疏松疗法，但这些疗法在骨折高风险个体中并未得到充分利用，因此需要更好的病例发现和骨折风险评估策略。人工智能 (AI) 和机器学习 (ML) 通过从医疗记录、成像和可穿戴设备中提取的高维数据中提取有用的特征，有望增强对骨折高风险个体的识别。 AI-ML 可以实现椎体骨折和骨质疏松症的自动机会筛查、针对生活方式因素的家庭监测和干预，以及整合多模式特征以利用骨折预测，最终有助于改善骨折风险评估和个体化治疗。乐观情绪必须与 AI-ML 模型的可解释性、偏差（包括数量上代表性不足的人群中的信息不公平）、模型局限性以及净临床效益和工作量影响相平衡。 AI-ML 算法的临床整合有可能改变骨质疏松症的管理，提供更个性化的方法来减轻骨质疏松性骨折的负担。