An adapted large language model facilitates multiple medical tasks in diabetes care
Lai Wei, Zhen Ying, Muyang He, Yutong Chen, Qian Yang, Yanzhe Hong, Jiaping Lu, Xiaoying Li, Weiran Huang, Ying Chen
2024-09-24
Summary
This paper discusses a new framework for training large language models (LLMs) specifically designed to help manage diabetes care. The study shows how these models can assist with various medical tasks related to diabetes.
What's the problem?
Diabetes is a serious health issue that requires careful management and collaboration among healthcare providers, patients, and caregivers. While LLMs have shown promise in healthcare, their effectiveness for specific diabetes-related tasks has not been fully explored. There is a need for specialized models that can understand and process the unique challenges of diabetes management.
What's the solution?
To address this, the researchers developed a comprehensive framework to create and validate diabetes-specific LLMs. They built a high-quality dataset by collecting, filtering, and refining data related to diabetes. Using this dataset, they fine-tuned LLMs to improve their understanding of various diabetes tasks. The study demonstrated that these specialized models could provide personalized healthcare advice, support medical education, and streamline clinical tasks effectively.
Why it matters?
This research is important because it enhances the ability of AI to assist in managing diabetes, which affects millions of people worldwide. By creating models that are specifically tailored for diabetes care, the study aims to improve patient outcomes and make healthcare more efficient. This could lead to better support for individuals living with diabetes and help healthcare professionals provide more effective care.
Abstract
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.