早期乳腺癌诊疗具有病程长、异质性强、数据多源异构及决策动态演变等特点，传统多学科诊疗（MDT）决策模式面临效率低、全周期管理断层等挑战。大语言模型（LLM）凭借处理非结构化文本、整合多源数据及辅助临床推理的优势，为解决上述问题提供了新路径。本文阐述LLM在乳腺癌MDT决策中的应用价值，重点分析个体化治疗推荐智能体、多智能体协同及临床决策证据生成与融合三种核心方法，并从临床知识库动态更新、病例数据库与模型深度融合、人机协作病例管理及类OpenClaw智能医生助理四个方面，提出未来发展建议，为推动乳腺癌全周期精准诊疗及MDT模式优化提供参考。

Early-stage breast cancer diagnosis and treatment are characterized by long disease course, high heterogeneity, multi-source and heterogeneous data, and dynamic evolving decision-making. The traditional multidisciplinary team (MDT) decision-making model faces challenges such as low efficiency and fragmented whole-cycle management. Large language models (LLMs) provide a new approach to solving these problems by virtue of their advantages in processing unstructured text, integrating multi-source data, and assisting clinical reasoning. This paper elaborated on the application value of LLMs in breast cancer MDT decision-making, focused on analyzing three core methods: personalized treatment recommendation agents, multi-agent collaboration, and clinical decision-making evidence generation and fusion. Furthermore, it proposed future development strategies across four dimensions: dynamic update of clinical knowledge bases, in-depth integration of case databases and models, human-machine collaborative case management, and OpenClaw-style intelligent physician assistant, providing references for promoting whole-cycle precise diagnosis and treatment of breast cancer and optimizing the MDT model.