Artificial intelligence-empowered multi-scale data integration for precision exercise biomedicine

Abstract

Abstract Exercise is a foundational intervention for preventing and managing chronic diseases, yet substantial inter-individual variation in physiological and molecular responses limits translation into personalized clinical practice. Meanwhile, wearables, multi-omics profiling, imaging phenotypes, and large-scale cohorts are generating multi-scale data that characterize exercise responses from molecular programs to real-world behavior. Artificial intelligence (AI) offers computational tools to integrate these modalities, learn predictive representations, and derive clinically relevant phenotypes. In this review, we summarize recent advances in AI-enabled translational exercise biomedicine. We first describe the evolving data ecosystem, including wearable-derived digital phenotypes, imaging biomarkers, and exercise-responsive multi-omics signatures. We then discuss key modeling paradigms – time-series learning, multimodal fusion, causal inference, and reinforcement learning – with emphasis on their roles in response prediction and personalized intervention design. We highlight emerging applications across cardiometabolic disease, metabolic liver disease, neurodegeneration, cancer survivorship, and chronic kidney disease. We also delineate persistent barriers to translation, including data heterogeneity, reliability under distribution shift, interpretability and uncertainty communication, and regulatory and ethical constraints. We argue that progress will depend on frameworks that link mechanistic insight with scalable digital monitoring and safety-aware adaptive prescriptions, enabling precision exercise biomedicine to move beyond empirical guidelines toward evidence-based, individualized decision-making.

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