From Prediction to Decision Support: A Critical Review and Six-Layer Framework for Responsible Artificial Intelligence in Sport Science

Abstract

Artificial intelligence is increasingly used in sport science to classify movement, analyse tactics, estimate readiness, forecast performance and injury risk, and support rehabilitation. Predictive performance alone, however, does not establish practical usefulness, safety, or responsible use. Through purposive source selection and critical synthesis, this review integrates sport-specific evidence with guidance on prediction modelling, human–AI interaction, and AI risk management. It highlights five recurring gaps: limited data and labels; leakage-prone or temporally inappropriate validation; weak reporting of calibration and uncertainty; unclear translation from prediction to action; and insufficient attention to athlete rights and post-deployment monitoring. We propose a six-layer framework covering data and context, prediction, decision translation, individualisation, human oversight, and deployment monitoring. For each applicable layer, the framework specifies evidence to document, appraisal questions, and common failure modes. It distinguishes model development from operational decision support and links technical evaluation with workflow utility, fairness, privacy, accountability, contestability, and lifecycle governance. The framework is conceptual, not a validated scoring or certification instrument. Sport-science AI should be evaluated as a socio-technical intervention rather than an isolated algorithm. Future studies should prioritise athlete-level and temporal validation, calibrated probabilities, external and prospective evaluation, prespecified action pathways, subgroup performance, documented override procedures, and ongoing monitoring.