Exploring the Role of Artificial Intelligence in Sports Injury Prevention and Rehabilitation

Abstract

This research examines the utilisation of artificial intelligence (AI) in sports damage anticipation and recovery, pointing to optimising competitor care and execution. Leveraging different datasets comprising execution measurements, biomechanical estimations, damage histories, physiological parameters, and natural components, four AI calculations were actualised and compared: Support Vector Machines (SVM), Random Forest, Recurrent Neural Networks (RNN), and Slope Boosting Machines (GBM). It comes about illustrating critical viability overall calculations, with RNN accomplishing the most elevated execution measurements. Exactness values for SVM, Irregular Timberland, RNN, and GBM were 0.85, 0.88, 0.90, and 0.87 separately, with comparing accuracy, recall, and F1-score values demonstrating strong prescient capabilities. These discoveries emphasise the potential of AI-driven approaches to precisely distinguish damage dangers and personalise recovery conventions custom-made to personal competitor needs. The comparative examination against existing strategies highlights the prevalent execution of AI calculations, emphasising the transformative effect of progressed advances in sports science and pharmaceuticals.