# Large Language Models in Sport Science & Medicine: Opportunities, Risks and Considerations

**Authors:** Mark Connor, Michael O'Neill  
**arXiv:** [2305.03851](https://arxiv.org/abs/2305.03851)  
**Date:** 2023-05-05  
**Summary page:** https://www.summarizepaper.com/en/paper/2305.03851v1/

## Key points

- Large language models (LLMs) have transformer-style architectures and are trained on extensive textual data
- LLMs can enhance the knowledge base of sports medicine practitioners, offer personalized training program recommendations, and disseminate high-quality information
- Opportunities include accessing cutting-edge insights and improving the quality of care provided to athletes, clients, and patients
- Risks associated with LLMs include biases in training data, data confidentiality concerns, potential for generating harmful or inaccurate output, and the importance of aligning models with human preferences through feedback mechanisms
- Further research is essential to navigate challenges effectively and ensure ethical use of LLMs in sports science and medicine.

## Summary

In their paper titled "Large Language Models in Sport Science & Medicine: Opportunities, Risks and Considerations," authors Mark Connor and Michael O'Neill delve into the potential applications of large language models (LLMs) in the fields of sports science and medicine. LLMs are sophisticated neural networks with transformer-style architectures that have been trained on extensive textual data and refined through human feedback. These models possess the capability to perform a wide range of natural language processing tasks. The authors highlight the promising opportunities that LLMs present in sports science and medicine. They suggest that these models could serve as valuable tools to enhance the knowledge base of sports medicine practitioners, offer personalized training program recommendations, and disseminate high-quality information to practitioners in developing countries. By leveraging LLMs, professionals in these fields may be able to access cutting-edge insights and improve the quality of care provided to athletes, clients, and patients. However, alongside these opportunities come inherent risks that must be carefully considered. The authors point out potential pitfalls associated with the use and development of LLMs, such as biases present in the training dataset used to create the model, concerns regarding data confidentiality, the possibility of generating harmful or inaccurate output, and the importance of aligning these models with human preferences through ongoing feedback mechanisms. To navigate these challenges effectively and ensure ethical use of LLMs in sports science and medicine, further research is deemed essential. It is crucial to gain a comprehensive understanding of how LLMs can be optimally applied in these domains while safeguarding against potential negative consequences. Ultimately, by addressing these considerations thoughtfully,Large Language Models have the potential to revolutionize practices within sports science and medicine for the benefit of athletes,cients,patients ,practitioners,and society at large.