大语言模型赋能乳腺外科精准决策与全周期病人管理OA

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.