Integrating Machine Learning for Personalized Fracture Risk Assessment: A Multimodal Approach

Abstract

We read with great interest the editorial titled "Clinical applicability of machine learning in family medicine" recently published in the Korean Journal of Family Medicine. 1)This editorial provides an overview of the potential applications of artificial intelligence (AI) and machine learning (ML) techniques in osteoporosis and fracture risk prediction.We appreciate the authors for highlighting this important and rapidly evolving area of research.As noted in the editorial, Kang et al. 2) presented a significant advancement in leveraging ML algorithms to predict fracture risk.By utilizing a large-scale cohort study that incorporates various risk factors such as bone mineral density and trabecular bone score data, their ML models demonstrated promising performance in predicting osteoporotic fractures compared with conventional risk assessment tools.While the editorial provides a comprehensive overview, we suggest additional perspectives and future directions in this field.

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