Strategic Management Model for the Implementation of Artificial Intelligence Technologies in the Prevention, Monitoring, and Rehabilitation of Elite Athletes’ Injuries

Introduction: This study aimed to develop a theoretical model for the strategic management of artificial intelligence (AI) implementation in the prevention and rehabilitation of injuries among elite athletes.
Methods: The research employed a qualitative approach using Strauss and Corbin’s systematic grounded theory methodology. Data were collected through in-depth semi-structured interviews with 16 experts, including sports medicine specialists, elite-level coaches, physiotherapists, performance analysts, and sport managers. Participants were selected through purposive and theoretical sampling, and data collection continued until theoretical saturation was achieved. Data analysis was conducted through open, axial, and selective coding.
Results: The findings led to the identification of key concepts and categories organized within a paradigmatic model. Causal conditions included the increasing complexity of sports injuries, limitations of traditional approaches, and the expansion of bio-performance data. Contextual conditions comprised digital infrastructure, data-driven culture, and organizational readiness. Intervening conditions such as data literacy, trust in algorithms, financial constraints, and ethical considerations played moderating roles. Action/interaction strategies involved continuous biological monitoring, predictive injury analytics, and personalized rehabilitation programs. Consequences were identified at multiple levels, including injury reduction, optimization of decision-making processes, and transformation of professional roles.
Conclusion: Selective coding resulted in the emergence of the core category: “Intelligent management of sports injuries through data-driven decision-making.” The theoretical model suggests that the effectiveness of AI implementation depends on the integrated alignment of technological, human, and organizational factors. The findings provide a theoretical foundation for policy development, technological interventions, and managerial frameworks in elite sport settings.