Artificial intelligence and wearable sensors in sports injury risk prediction: current status and future perspectives.
Clinical Snapshot
PICO Framework
| P — Population | Athletes and individuals engaged in sports activities |
| I — Intervention | Artificial intelligence algorithms and wearable sensor technologies (IMUs, EMG, physiological monitors) |
| C — Comparator | Traditional injury risk assessment methods or no intervention |
| O — Outcomes | Sports injury risk prediction accuracy, injury prevention effectiveness |
Bottom Line
This narrative review explores the potential of combining artificial intelligence with wearable sensor technology for sports injury prediction. While the topic is highly relevant to modern sports medicine, the review lacks the systematic methodology required for evidence-based clinical decision-making. The authors acknowledge significant challenges including data heterogeneity, limited generalizability, and compliance issues. Without quantitative synthesis, quality assessment, or systematic evaluation of prediction accuracy, clinicians cannot determine the clinical utility of these technologies. The review serves as a useful overview of emerging technologies but provides insufficient evidence to support implementation in clinical practice. More rigorous systematic reviews and well-designed validation studies are needed before these technologies can be recommended for routine injury prevention programs.
Key Findings
P Value: Not applicable - narrative review
Effect Size: Not quantified - qualitative assessment only
Primary Outcome: Sports injury risk prediction using AI and wearable sensors
Nnt Or Sensitivity: Prediction accuracy metrics not systematically reported
Confidence Interval: Not reported
Clinical Application
Limited by technology costs, compliance issues, and need for specialized expertise Relevant to Sports Medicine Australia guidelines and high-performance sport programs, though cost-effectiveness unclear for widespread implementation Athletes across various sports, potentially applicable to recreational sports participants
Abstract
BACKGROUND: Artificial intelligence (AI) and wearable sensors are increasingly reshaping sports injury risk prediction by enabling continuous, individualized, and data-driven assessment. DISCUSSION: This review summarizes recent advances in wearable technologies - including inertial measurement units (IMUs), electromyography (EMG), physiological monitors, and flexible electronics - and their use in capturing biomechanical, physiological, and psycho-physiological indicators relevant to injury risk. Deep learning (DL) models, particularly those capable of temporal and multimodal fusion, have shown promise in predicting injuries such as anterior cruciate ligament (ACL) tears, muscle fatigue, and stress fractures. Through representative applications and mechanism-informed analysis, we highlight the growing role of interpretable AI, real-time feedback, and behavior-integrated predictive systems. CONCLUSIONS: Key challenges remain, including data heterogeneity, limited generalizability, compliance issues, and privacy concerns. Future directions point toward personalized modeling, explainable systems, federated learning, and digital twin frameworks. Together, these advancements mark a shift toward proactive, intelligent injury prevention across athletic and clinical settings.
References
- 1.Dong, G., Tan, X., Yuan, P., & Li, Y. (2026). Artificial intelligence and wearable sensors in sports injury risk prediction: current status and future perspectives. Annals of Medicine. https://doi.org/10.1080/07853890.2026.2658879
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