Use of artificial intelligence for optimizing sports performance: a PRISMA 2020 systematic review (2020-2025)
DOI:
https://doi.org/10.70577/asce.v5i1.739Keywords:
Artificial intelligence, Sports performance, Machine learning, Data analysis, Training monitoring, Injury prevention.Abstract
In recent years, the integration of artificial intelligence (AI) into sports science has increased, generating a large body of research on performance optimization. However, the available evidence is fragmented, with heterogeneous methodologies, diverse AI techniques, and variable outcomes related to physical, biomechanical, and tactical performance. This fragmentation has limited a comprehensive understanding of AI's role in enhancing athletic performance. The aim of this study was to systematically analyze the scientific evidence published between 2020 and 2025 on the use of artificial intelligence for optimizing athletic performance, identifying the main AI techniques applied, their contexts of use, and the performance variables studied. Following the PRISMA 2020 guidelines, 21 peer-reviewed studies were included. The reviewed studies showed improvements in key performance-related variables, such as training load management, movement efficiency, injury risk detection, predictive accuracy of performance outcomes, and strategic decision-making in competitive environments. Evidence shows that AI is an effective tool for optimizing athletic performance in various areas. However, limitations related to methodological heterogeneity, data quality, and ethical considerations have been identified. Standardized methodologies, longitudinal validation, and interdisciplinary collaboration were essential to ensure the sustainable and responsible integration of AI into sports science.
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