Large language models (LLMs) primarily trained on English texts, often face biases and inaccuracies in Chinese contexts. Their limitations are pronounced in fields like Traditional Chinese Medicine (TCM), where cultural and clinical subtleties are vital, further hindered by a lack of domain-specific data, such as rheumatoid arthritis (RA). To address these issues, this paper introduces Hengqin-RA-v1, the first large language model specifically tailored for TCM with a focus on diagnosing and treating RA. We also present HQ-GCM-RA-C1, a comprehensive RA-specific dataset curated from ancient Chinese medical literature, classical texts, and modern clinical studies. This dataset empowers Hengqin-RA-v1 to deliver accurate and culturally informed responses, effectively bridging the gaps left by general-purpose models. Extensive experiments demonstrate that Hengqin-RA-v1 outperforms state-of-the-art models, even surpassing the diagnostic accuracy of TCM practitioners in certain cases.

本研究针对大型语言模型在中文语境中，特别是在传统中医领域的偏见和不准确性问题，提出了恒琴-RA-v1模型。该模型基于一个专为类风湿性关节炎开发的综合数据集，从古代中医文献和现代临床研究中汇编而成，显著提高了诊断和治疗的准确性，超过了某些情况下传统中医从业者的诊断准确率。

恒琴-RA-v1：用于类风湿性关节炎诊断和治疗的高级大语言模型，基于数据集的传统中医