Large Language Models in Sport Science & Medicine: Opportunities, Risks
  and Considerations
Mark Connor Michael O'Neill
This paper explores the potential opportunities, risks, and challenges
associated with the use of large language models (LLMs) in sports science and
medicine. LLMs are large neural networks with transformer style architectures
trained on vast amounts of textual data, and typically refined with human
feedback. LLMs can perform a large range of natural language processing tasks.
In sports science and medicine, LLMs have the potential to support and augment
the knowledge of sports medicine practitioners, make recommendations for
personalised training programs, and potentially distribute high-quality
information to practitioners in developing countries. However, there are also
potential risks associated with the use and development of LLMs, including
biases in the dataset used to create the model, the risk of exposing
confidential data, the risk of generating harmful output, and the need to align
these models with human preferences through feedback. Further research is
needed to fully understand the potential applications of LLMs in sports science
and medicine and to ensure that their use is ethical and beneficial to
athletes, clients, patients, practitioners, and the general public.