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

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. Keywords First keyword Second keyword More 1. Introduction Large language models (LLMs) have emerged as a powerful tool in the field of artificial intelligence. These models are trained on the vast amounts of textual data readily available on the internet, using transformer architectures that contain hundreds of billions of parameters [1, 2, 3]. As a result, they are capable of performing a range of natural language processing tasks, including text summarization and generation, language translation, conversational dialogue, and code generation. While the use of artificial intelligence technology in sports science & medicine is steadily increasing, the potential applications of LLMs in this field remain largely unexplored. This article aims to examine the opportunities, risks, and challenges associated with the use of LLMs in sports science and medicine. Opportunities LLMs have the potential to transform various aspects of sports science and medicine. The development of recent fine-tuned instruction response models like ChatGPT has provided this technology with a suitable interface to support and augment the knowledge of its users. Early work is already underway to fine-tune these models for specialised domains. One relevant example is ChatDoctor, a LLM fine-tuned on a curated dataset of real-world conversations between patients and doctors [4]. This specialised LLM is designed to support initial diagnosis and triage of patients. Conceivably a similar model could be developed to assist sports medicine practitioners by fine-tuning on a specialised dataset of electronic medical records, clinical notes, sports science and medicine literature and in the case of multi-model models, medical images.