An adapted large language model facilitates multiple medical tasks in
  diabetes care
Authors: name: Lai Wei, name: Zhen Ying, name: Muyang He, name: Yutong Chen, name: Qian Yang, name: Yanzhe Hong, name: Jiaping Lu, name: Xiaoying Li, name: Weiran Huang, name: Ying Chen

Diabetes is a chronic disease that poses a significant global health burden,
and optimizing diabetes management requires multi-stakeholder collaboration.
Large language models (LLMs) have shown promise in various healthcare
scenarios, but their effectiveness across a diverse range of diabetes tasks
remains unproven. In this study, we introduced a framework to train and
validate diabetes-specific LLMs. We first developed a comprehensive data
processing pipeline that includes data collection, filtering, augmentation and
refinement. This approach contributes to creating a high-quality,
diabetes-specific dataset, and several evaluation benchmarks entirely from
scratch. Utilizing the collected training dataset, we fine-tuned a
diabetes-specific LLM family that demonstrated state-of-the-art proficiency in
understanding and processing various diabetes tasks compared to other LLMs.
Furthermore, clinical studies showed the potential applications of our models
in diabetes care, including providing personalized healthcare, assisting
medical education, and streamlining clinical tasks. In conclusion, our study
introduced a framework to develop and evaluate a diabetes-specific LLM family,
and highlighted its potential to enhance clinical practice and provide
personalized, data-driven support for diabetes support when facing different
end users. The code is provided via GitHub at
https://github.com/waltonfuture/Diabetica.