JingFang: An Expert-Level Large Language Model for Traditional Chinese Medicine Clinical Consultation and Syndrome Differentiation-Based Treatment
This work addresses the need for more reliable AI-assisted clinical consultation and treatment in traditional Chinese medicine, representing a domain-specific incremental improvement.
The authors tackled the problem of incomplete clinical consultation and inaccurate syndrome differentiation in existing large language models for traditional Chinese medicine by developing JingFang, which achieved expert-level performance in these tasks.
The effective application of traditional Chinese medicine (TCM) requires extensive knowledge of TCM and clinical experience. The emergence of Large Language Models (LLMs) provides a solution to this, while existing LLMs for TCM exhibit critical limitations of incomplete clinical consultation and diagnoses, as well as inaccurate syndrome differentiation. To address these issues, we establish JingFang (JF), a novel TCM LLM that demonstrates the level of expertise in clinical consultation and syndrome differentiation. We propose a Multi-Agent Collaborative Chain-of-Thought Mechanism (MACCTM) for comprehensive and targeted clinical consultation, enabling JF with effective and accurate diagnostic ability. In addition, a Syndrome Agent and a Dual-Stage Recovery Scheme (DSRS) are developed to accurately enhance the differentiation of the syndrome and the subsequent corresponding treatment. JingFang not only facilitates the application of LLMs but also promotes the effective application of TCM for healthcare.