Sport science faces a critical challenge in how to properly evaluate artificial intelligence (AI) and wearable technologies that generate training data and recommendations in ways traditional assessment methods cannot capture. Contemporary training programs increasingly incorporate real-time physiological monitoring systems and wearable sensors that generate vast quantities of previously inaccessible data. These data are increasingly being analyzed by machine-learning algorithms and computer-vision platforms.1 AI-enhanced interventions integrate evidence-based algorithms in order to guide training. Laboratory-based testing (eg, VO2peak measurement) and randomized controlled trials (RCTs) often inadequately address training interventions characterized by continuous adaptation and individualized outcomes.2 For instance, AI-driven training systems adjust daily workout intensity based on real-time recovery markers, whereas traditional protocols prescribe fixed progressions regardless of individual wellness and readiness to train. This challenge requires new standardization protocols that validate algorithmic systems while ensuring safety and practical utility in real-world training.
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