Artificial intelligence applications in sport-related concussion: an updated scoping review

Abstract

ObjectivesSport-related concussion (SRC) is a complex mild traumatic brain injury for which diagnosis, monitoring, and prognosis remain largely reliant on subjective clinical assessment.Artificial intelligence (AI) has emerged as a potential tool to enhance objectivity by integrating highdimensional, multimodal data across the SRC care pathway.Design: Scoping review. MethodsA systematic literature search was conducted across six databases (MEDLINE, EMBASE, SPORTDiscus, Scopus, Web of Science, and Cochrane Central) from inception to December 2025.Eligible studies were classified into four domains: Detection & Diagnosis, Monitoring & Surveillance, Prognosis & Recovery, and Prevention & Risk Modelling.Results Fifty-five studies met the inclusion criteria, with over 80% published since 2020.Detection & Diagnosis was the most represented domain, primarily leveraging EEG, speech, motor, and multimodal clinical data.Monitoring & Surveillance studies focused on wearable sensors, mouthguards, and video-based impact detection to quantify exposure and reduce false positives.Prognosis & Recovery models explored recovery trajectories, persistent symptoms, and reinjury risk, while Prevention & Risk Modelling studies predominantly relied on biomechanical and finite element-derived data to estimate injury risk.Despite promising performance, studies were highly heterogeneous and frequently limited by small or imbalanced samples, inconsistent outcome definitions, limited external validation, and poor model interpretability Conclusion AI-based approaches show growing potential to support SRC management across multiple clinical domains.However, current evidence supports their use primarily as decision-support tools.Future research should prioritise large, multicentre studies, transparent labelling strategies, explainable AI frameworks, and rigorous external validation to enable safe and clinically meaningful implementation.

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