Use of machine learning and deep learning for exercise prescription

Introduction: artificial intelligence is revolutionizing exercise prescription in sports physiotherapy by offering more personalized and data-driven approaches. Through machine learning and deep learning algorithms, AI enables the analysis of complex variables such as biomechanics, physiological responses, and the patient's clinical history, dynamically adjusting exercise programs. This optimizes performance, prevents injuries, and enhances rehabilitation. Methodology: a systematic review of studies on the use of AI in sports physiotherapy, based on articles published between 2015 and 2024, using specific inclusion criteria. The findings highlight the benefits of AI in personalizing exercise programs, emphasizing its capacity to improve adherence, load dosing, and injury prevention. However, clinical implementation of AI faces challenges such as external model validation, result interpretability, and the ethical management of sensitive data. Discussion: the review results show that AI is transforming exercise prescription in sports physiotherapy through a personalized and data-driven approach. AI algorithms, such as machine learning and deep learning, allow for the analysis of complex variables like biomechanics, physiological responses, and clinical history, dynamically adjusting exercise programs. Nevertheless, significant challenges remain for its clinical implementation, including external validation of models, interpretability of outcomes, and ethical concerns in handling sensitive data. Conclusion: AI holds tremendous potential to transform sports physiotherapy, but its integration into clinical practice requires overcoming technical and ethical challenges. Model validation, healthcare professional training, and equitable access to these technologies are essential aspects to ensure effective and safe implementation. Future research should address these challenges to maximize the benefits of AI in the field of exercise.