Fine-Tuning the Llama2 Large Language Model Using Books on the Diagnosis and Treatment of Musculoskeletal System in Physical Therapy

ABSTRACT Backgroud Generative language models (GLM) utilize machine learning algorithms to perform various tasks such as text generation, question response, and sentence completion by imitating the language that humans understand and use. Purpose This study was to fine-tune the Llama2 language model using text data from books on the diagnosis and treatment of musculoskeletal system in physical therapy and compare it to the base model to determine its usability in medical fields. Results Compared to the base model, the fine-tuned model consistently generated answers specific to the musculoskeletal system diagnosis and treatment, demonstrating improved understanding of the specialized domain. Conclusion The model fine-tuned for musculoskeletal diagnosis and treatment books provided more detailed information related to musculoskeletal topics, and the use of this fine-tuned model could be helpful in medical education and the acquisition of specialized knowledge.