Systematic review of different approaches for performance enhancement in elite sport

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. Conclusion 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.