AI & Machine Learning for Injury Prediction: From Data to Preventive Strategies

The integration of AI and Machine Learning into injury prediction is transforming how researchers, clinicians, and sports professionals assess and manage risk. By combining computational modeling with biomechanical, physiological, and clinical data, these approaches offer new possibilities for identifying risk factors and implementing targeted prevention strategies.This Research Topic invites interdisciplinary contributions focused on the development, training, and deployment of machine learning algorithms, deep learning architectures, and advanced data-fusion methods designed to predict and prevent injuries. We welcome research exploring how AI can process and integrate heterogeneous, multi-modal datasets, ranging from high-frequency biomechanical waveforms and physiological telemetry to clinical history and occupational safety data. By moving beyond traditional linear statistical analyses, these advanced machine learning models can uncover complex, non-linear relationships, automate feature extraction, and identify multi-factorial risk profiles across athletic, clinical, and occupational contexts.The scope of this collection centers on the methodological and practical integration of AI-driven analytics within computational biomechanics, sports science, and clinical medicine. We specifically seek empirical and applied investigations that validate machine learning models in real-world environments, directly linking algorithmic predictions to actionable clinical decisions or on-field preventive adjustments. To guide prospective contributors, this Research Topic focuses on the key stages of the AI-driven predictive pipeline, from raw data ingestion to clinical translation, as outlined in the themes below.To gather further insights within these boundaries, we welcome articles addressing, but not limited to, the following themes:- Predictive modeling and risk assessment using AI and Machine Learning architectures- Wearable sensor data integration and real-time performance monitoring for early, AI-driven injury detection- Translational frameworks bridging AI data science and clinical or sports practice- AI model validation, reproducibility, and ethical considerations (e.g., algorithmic bias and data privacy) in predictive sports medicine- Personalized and preventive strategies and automated interventions informed by predictive analytics and machine learning modelsWe encourage submissions in the form of Original Research, Systematic Reviews, Meta-Analyses, Narrative Reviews, and Perspective articles. Topic editors: Felipe Hermosilla-Palma, Yang Renhao Source: https://www.frontiersin.org/research-topics/81873/ai-machine-learning-for-injury-prediction-from-data-to-preventive-strategies