SYSTEMATIC REVIEW article
Front. Artif. Intell.
Sec. Machine Learning and Artificial Intelligence
Systematic Review of Different Approaches for Performance Enhancement in Elite Sport
Provisionally accepted
- Université Hassan II Mohammedia, Mohammedia, Morocco
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Background: Elite sport is undergoing rapid technological transformation driven by advanced analytics, artificial intelligence (AI), and immersive systems. While numerous studies address performance enhancement and injury-related applications, evidence remains fragmented across technologies and sport contexts. Objective: This systematic review aimed to examine the prevalence and distribution of advanced analytical technologies across application domains (performance, injury, and emerging objectives) and sport disciplines, and to identify areas of technological maturity in elite sport. Methods: A systematic review was conducted following PRISMA 2020 guidelines. Four databases (Google Scholar, Scopus, Web of Science, IEEE Xplore) were searched for peer-reviewed studies published between January 2019 and March 2025. Fifty-two studies met the inclusion criteria and were synthesised using a structured qualitative approach. Results: AI-based methods dominated the literature (32/52 studies, 61.5%), including machine learning (15.4%), deep learning (9.6%), generative AI (17.3%), and hybrid approaches (19.2%). Statistical modelling accounted for 23.1% of studies, while virtual reality represented 15.4%. Performance enhancement was the primary objective (52%), followed by injury-related outcomes (27%) and emerging applications such as tactical analysis and decision support (21%). Team sports, particularly football, demonstrated the highest level of technological maturity. Conclusions: Advanced analytical technologies are unevenly distributed across sport disciplines and objectives, with clear maturity in performance-focused team sport applications. These findings provide evidence-based guidance for researchers and practitioners seeking to prioritise effective and context-appropriate technological adoption in elite sport.
Keywords: artificial intelligence, deep learning, elite sport, injury prevention, machine learning
Received: 06 Jan 2026; Accepted: 12 Feb 2026.
Copyright: © 2026 Dehbane, OUAHABI and EL FIlALI. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Oualid Dehbane
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