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TRUVACE RECORD VERSION
record: TRV-2026-0141
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-07-13T08:49:40.038002Z
status: published
lens: trace
sector: sports
headline: Artificial intelligence applications in sport-related concussion: an updated scoping review
dek: 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)…
gain_title: 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.
problem_title: Current AI studies for sport-related concussion are frequently constrained by small or imbalanced samples, inconsistent definitions, limited external validation, and poor model interpretability.
trace_subject: AI applications for sport-related concussion management
gain_reading: 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.
gain_evidence: reduce false-positive events | to quantify exposure and reduce false-positive events
problem_reading: Current AI studies for sport-related concussion are frequently constrained by small or imbalanced samples, inconsistent definitions, limited external validation, and poor model interpretability.
problem_evidence: limited by small or imbalanced samples | limited external validation, and poor model interpretability
quick_read: 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.
limitation: Evidence base is heterogeneous with small or imbalanced samples, inconsistent outcome definitions, limited external validation, and poor interpretability, supporting use only as decision-support.
tag: Automated dual reading
key_points: 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.
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.
sources:
- peer_reviewed | Journal of Science and Medicine in Sport | https://doi.org/10.1016/j.jsams.2026.05.002 | 2026-05-01
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