From Reactive to Proactive: Empowering Sports Injury Prevention with Large Language Models - Wang, Jianlong; Guo, Tianao; Xu, Zihan (International Journal of Public Health and Medical Research, 2026)
Sports injury management is undergoing a paradigm shift from ‘reactive post-event intervention’ to ‘proactive pre-event prevention’. Although traditional machine learning (ML) and deep learning (DL) have demonstrated some efficacy in identifying injury risk factors, they still face analytical bottlenecks when dealing with complex, heterogeneous, and multimodal sports data. This paper aims to systematically explore the current applications of large language models (LLMs) in competitive sports injury prevention, the optimisation pathways for core technologies, and the empirical challenges they face in complex clinical decision-making. It examines how Retrieval-Augmented Generation (RAG), Parameter-Efficient Fine-Tuning (PEFT), and Multimodal Large Language Models (MLLMs) can bridge the general language barrier to adapt to specialised knowledge in sports medicine. Although LLMs possess powerful natural language processing and multimodal data integration capabilities, issues such as data privacy, model hallucinations, out-of-distribution (OOD) robustness, and insufficient explainability limit their independent application in high-risk medical decision-making. Future research needs to be deepened in the directions of edge computing deployment and explainable artificial intelligence (XAI).