Department of Physical Therapy, Federal Institute of Rio de Janeiro, Rio de Janeiro, Brazil; Pain in Motion Research Group, Department of Physical Therapy, Human Physiology and Anatomy, Faculty of Physical Education & Physiotherapy, Vrije Universiteit Brussel, Brussels, Belgium; School of Physical and Occupational Therapy, McGill University, Montreal, Canada.
Nucleus of Neuroscience and Behavior and Nucleus of Applied Neuroscience, Universidade de Sao Paulo (USP), Sao Paulo, Brazil; Research, Technology, and Data Science Office, Grupo Superador, Sao Paulo, Brazil.
Braz J Phys Ther. 2024 May-Jun;28(3):101083. doi: 10.1016/j.bjpt.2024.101083. Epub 2024 May 21.
The development and application of Artificial Intelligence (AI) and Machine Learning (ML) in healthcare have gained attention as a promising and powerful resource to change the landscape of healthcare. The potential of these technologies for injury prediction, performance analysis, personalized training, and treatment comes with challenges related to the complexity of sports dynamics and the multidimensional aspects of athletic performance.
We aimed to present the current state of AI and ML applications in sports science, specifically in the areas of injury prediction, performance enhancement, and rehabilitation. We also examine the challenges of incorporating AI and ML into sports and suggest directions for future research.
We conducted a comprehensive literature review, focusing on publications related to AI and ML applications in sports. This review encompassed studies on injury prediction, performance analysis, and personalized training, emphasizing the AI and ML models applied in sports.
The findings highlight significant advancements in injury prediction accuracy, performance analysis precision, and the customization of training programs through AI and ML. However, future studies need to address challenges such as ethical considerations, data quality, interpretability of ML models, and the integration of complex data.
AI and ML may be useful for the prevention, detection, diagnosis, and treatment of health conditions. In this Masterclass paper, we introduce AI and ML concepts, outline recent breakthroughs in AI technologies and their applications, identify the challenges for further progress of AI systems, and discuss ethical issues, clinical and research opportunities, and future perspectives.
人工智能（AI）和机器学习（ML）在医疗保健领域的发展和应用引起了关注，它们是改变医疗保健格局的有前途和强大的资源。这些技术在损伤预测、性能分析、个性化训练和治疗方面的潜力，与运动动态的复杂性和运动表现的多维方面相关的挑战并存。
我们旨在介绍 AI 和 ML 在运动科学中的应用现状，特别是在损伤预测、性能增强和康复领域。我们还检查了将 AI 和 ML 纳入运动的挑战，并提出了未来研究的方向。
我们进行了全面的文献综述，重点关注与 AI 和 ML 在运动中的应用相关的出版物。本综述包括了关于损伤预测、性能分析和个性化训练的研究，强调了应用于运动的 AI 和 ML 模型。
研究结果突出了 AI 和 ML 在损伤预测准确性、性能分析精度和通过 AI 和 ML 定制训练计划方面的显著进展。然而，未来的研究需要解决一些挑战，如伦理考虑、数据质量、ML 模型的可解释性以及复杂数据的整合。
AI 和 ML 可能有助于预防、检测、诊断和治疗健康状况。在本硕士论文中，我们介绍了 AI 和 ML 概念，概述了 AI 技术的最新突破及其应用，确定了 AI 系统进一步发展的挑战，并讨论了伦理问题、临床和研究机会以及未来展望。
