JMIR Publications

ABSTRACT

Artificial intelligence (AI) holds significant potential to transform exercise health through applications like personalized training, injury prevention, and targeted rehabilitation. However, the scarcity of high-quality, real-world data—due to issues like data fragmentation, inconsistent formats, privacy constraints, and confidentiality concerns—severely restricts AI development and deployment in this domain. Learning from advancements in healthcare AI, strategies such as federated learning for privacy-preserving collaboration, synthetic data generation to augment limited datasets, transfer learning to leverage existing models, and structured data-sharing protocols offer viable solutions. Furthermore, an innovative closed-loop data generation process is proposed: leveraging large language model APIs to create extensive synthetic datasets from initial high-quality inputs, followed by rigorous human expert validation and refinement through real-world feedback. This paper argues that overcoming the data bottleneck necessitates an integrated strategy combining these methods. Implementing such comprehensive measures will enhance AI model robustness and generalizability, ultimately accelerating the delivery of tangible benefits to athletes and health practitioners.