📄 Abstract
To explore the prospects and challenges of applying artificial intelligence and its machine learning subfield in sports medicine, to drive knowledge innovation in this domain. Research Content includes Applications of machine learning in sports medicine: Clustering and classifying athlete data, developing predictive models to optimize training and prevent injuries, and providing interpretable decision support for medical professionals. Challenges of machine learning in sports medicine: Issues with data availability and quality, model interpretability and transparency, as well as the integration with existing workflows. In summary, the potential of AI and machine learning in sports medicine is immense, but to fully harness their transformative value, interdisciplinary collaboration, data sharing, rigorous validation, and the establishment of ethical guidelines are essential. Only through these collective efforts can the field optimize athlete training, prevent injuries, and drive overall innovation in sports medicine.
🏷️ Keywords
📚 How to Cite:
Katherine Ning LI , THE PROGRESS IN THE RESEARCH OF MACHINE LEARNING IN SPORTS MEDICINE , Volume 10 , Issue 4, april 2024, EPRA International Journal of Multidisciplinary Research (IJMR) , DOI: https://doi.org/10.36713/epra16367