Why the study?
The increasing use of AI in athlete health monitoring and injury prediction presents complex ethical challenges alongside technological opportunities.
Population
24 empirical and conceptual studies on AI-driven injury forecasting systems across diverse sports disciplines
Design
Narrative review
Key result
A review of 24 studies on AI-driven injury prediction in sports identified five dominant ethical concerns, highlighting a lack of robust ethical safeguards and athlete-centered governance structures.
Authors
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Ethical gaps caution against routine AI injury prediction in sports; leaves open need for athlete-centered governance frameworks.
The integration of AI in sports medicine risks reinforcing structural inequalities and undermining athlete autonomy without sport-specific ethical frameworks and enforceable data rights.
Waśkiewicz et al. (2025) conducted a review in AI-driven injury prediction in sport (n=24). AI-driven injury forecasting systems was evaluated on Ethical concerns (privacy and data protection, algorithmic fairness, informed consent, athlete autonomy, and long-term data governance). A review of 24 studies on AI-driven injury prediction in sports identified five dominant ethical concerns, highlighting a lack of robust ethical safeguards and athlete-centered governance structures.