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Injury Prediction and Risk Modelling in Team Sports Using Artificial Intelligence and Sensor-Based Monitoring: A
Michail Tsenos1, Christos Kokkotis2, Dimitrios Draganidis3
1Department of Informatics, Athens University of Economics and Business, 10434 Athens, Greece.
Journal of Functional Morphology and Kinesiology
|May 27, 2026
Summary
Artificial intelligence (AI) and sensor data can predict team sport injuries. However, diverse methods and limited validation hinder reliable injury risk models, requiring multi-centre studies for practical application.
Area of Science:
- Sports Medicine
- Data Science
- Biomechanics
Background:
- Sports injuries significantly impact athletes and teams.
- AI and sensor technology offer potential for injury prediction.
- Methodological diversity in current research limits consistent conclusions.
Purpose of the Study:
- To map evidence on AI and sensor use for injury prediction in team sports.
- To identify trends and research gaps in AI-driven injury risk modeling.
- To guide future development of reliable injury prediction systems.
Main Methods:
- Scoping review following PRISMA-ScR guidelines.
- Systematic searches in PubMed and Scopus databases.
- Analysis of 11 eligible studies on AI/ML for injury prediction using sensor data.
Main Results:
- Most studies focused on football/rugby, using wearable GPS/inertial sensor data.
- External workload, injury history, and recovery markers were key predictors.
- Significant heterogeneity in methodologies, validation, and performance metrics was observed.
Conclusions:
- Current AI models for injury prediction are limited by methodological diversity and lack of external validation.
- Future research needs multimodal data integration and multi-centre validation.
- Developing interpretable and practical AI-based injury prediction systems is crucial.