Artificial intelligence and Machine Learning approaches in sports: Concepts, applications, challenges, and future perspectives.

Autor: Reis FJJ; 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. Electronic address: felipe.reis@ifrj.edu.br., Alaiti RK; 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., Vallio CS; Health Innovation, Data Science, and MLOps Semantics, São Paulo, Brazil., Hespanhol L; Department of Physical Therapy, Faculty of Medicine, University of Sao Paulo (USP), Sao Paulo, Brazil; Amsterdam Collaboration on Health & Safety in Sports, Department of Public and Occupational Health, Amsterdam Movement Sciences, Amsterdam University Medical Centers (UMC) location VU University Medical Center Amsterdam (VUmc), Amsterdam, the Netherlands.
Jazyk: angličtina
Zdroj: Brazilian journal of physical therapy [Braz J Phys Ther] 2024 May-Jun; Vol. 28 (3), pp. 101083. Date of Electronic Publication: 2024 May 21.
DOI: 10.1016/j.bjpt.2024.101083
Abstrakt: Background: 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.
Objectives: 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.
Method: 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.
Results: 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.
Conclusion: 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.
Competing Interests: Declaration of competing interest The authors declare no competing interest.
(Copyright © 2024 Associação Brasileira de Pesquisa e Pós-Graduação em Fisioterapia. Publicado por Elsevier España, S.L.U. All rights reserved.)
Databáze: MEDLINE
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