Shared Decision Making (SDM), as a modern medical decision-making model emphasizing patient participation, faces multidimensional challenges in China, including uneven distribution of medical resources, knowledge gaps, and inadequate cultural adaptation. The implementation of SDM in China is hindered by time constraints, insufficient patient willingness to participate, a lack of standardized decision support tools, and structural barriers such as healthcare reimbursement systems. Large Language Models (LLMs), with their powerful natural language processing capabilities, demonstrate unique advantages in enhancing communication efficiency, supporting personalized decision-making, and promoting multi-party collaboration. Key functionalities such as information integration, personalized support tools, and sentiment analysis significantly improve patient engagement and decision quality. However, LLMs still face limitations in localization, decision-chain completeness, and handling complex scenarios, particularly in understanding traditional Chinese medicine (TCM) knowledge and supporting family-oriented decision-making models. Future efforts should focus on constructing integrated knowledge graphs of biomedicine and Traditional Chinese Medicine, optimizing multi-layered expression capabilities, and improving model interpretability to promote LLMs' in-depth application in SDM within China, ultimately enhancing healthcare quality and patient satisfaction.