Large Language Models in Integrative Medicine: Progress, Challenges, and Opportunities
Hiu Fung Yip, Zeming Li, Lu Zhang, Aiping Lyu
Abstract
Integrating Traditional Chinese Medicine (TCM) and Modern Medicine faces significant barriers, including the absence of unified frameworks and standardized diagnostic criteria. While Large Language Models (LLMs) in Medicine hold transformative potential to bridge these gaps, their application in integrative medicine remains underexplored and methodologically fragmented. This review systematically examines LLMs' development, deployment, and challenges in harmonizing Modern and TCM practices while identifying actionable strategies to advance this emerging field. This review aimed to provide insight into the following aspects. First, it summarized the existing LLMs in the General Domain, Modern Medicine, and TCM from the perspective of their model structures, number of parameters and domain-specific training data. We highlighted the limitations of existing LLMs in integrative medicine tasks through benchmark experiments and the unique applications of LLMs in Integrative Medicine. We discussed the challenges during the development and proposed possible solutions to mitigate them. This review synthesizes technical insights with practical clinical considerations, providing a roadmap for leveraging LLMs to bridge TCM's empirical wisdom with modern medical systems. These AI-driven synergies could redefine personalized care, optimize therapeutic outcomes, and establish new standards for holistic healthcare innovation.