aceAI applications for sport-related concussion management
Source article: Artificial intelligence applications in sport-related concussion: an updated scoping review
OBJECTIVES: Sport-related concussion is a complex mild traumatic brain injury for which diagnosis, monitoring, and prognosis remain largely dependent on subjective clinical assessment. Artificial intelligence has emerged as a potential tool to enhance objectivity by integrating large, multimodal datasets across the concussion care pathway. DESIGN: Scoping review. METHODS: A systematic literature search was conducted across six databases (MEDLINE, EMBASE, SPORTDiscus, Scopus, Web of Science, and Cochrane Central)…
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By December 2025, a scoping review of six databases identified 55 studies of artificial intelligence across the concussion care pathway, from detection and diagnosis using EEG, speech and motor data to monitoring with wearables, mouthguards and video, plus prognosis and prevention modeling.
The findings matter because concussion care still relies on subjective assessment, and objective AI support could improve detection and exposure tracking, but the review shows the field has not yet achieved validated, interpretable tools ready for standalone clinical use.
- Scoping review searched six databases from inception to December 2025 and included 55 studies.
- Studies were grouped into Detection & Diagnosis, Monitoring & Surveillance, Prognosis & Recovery, and Prevention & Risk Modeling.
- Detection & Diagnosis was the most represented domain leveraging electroencephalography, speech, motor, and multimodal clinical data.
- Monitoring & Surveillance work used wearable sensors, mouthguards, and video-based impact detection to quantify exposure.
AI models using EEG, speech, motor data, wearables and video have been applied to detect concussion and quantify head-impact exposure while reducing false-positive events.
Current AI studies for sport-related concussion are frequently constrained by small or imbalanced samples, inconsistent definitions, limited external validation, and poor model interpretability.
The rundown
The review classified 55 eligible studies into four domains and found Detection & Diagnosis most common, followed by work on wearable sensors and mouthguards for surveillance, models of recovery trajectories and reinjury risk, and biomechanical and finite element-derived data for risk modeling.
Authors concluded that despite promising performance, heterogeneity and methodological limits mean AI should remain a decision-support tool, calling for large multicenter studies, transparent labeling, explainable AI frameworks, and rigorous external validation.
Evidence base is heterogeneous with small or imbalanced samples, inconsistent outcome definitions, limited external validation, and poor interpretability, supporting use only as decision-support.
Sources
- Peer-reviewedJournal of Science and Medicine in Sport2026-05-01
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