外科理论与实践2026，Vol.31Issue(3)：185-190,6.DOI:10.16139/j.1007-9610.2026.03.02
大语言模型赋能乳腺外科精准决策与全周期病人管理
Large language models empower precision decision-making in breast surgery and whole-cycle patient management
摘要
Abstract
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.关键词
大语言模型/乳腺癌/多学科诊疗决策/精准诊疗/全周期管理Key words
Large language model(LLM)/Breast cancer/Multidisciplinary team(MDT)decision-making/Precise diagnosis and treatment/Whole-cycle management分类
医药卫生引用本文复制引用
朱能军,曹健,陈小松,朱思吉..大语言模型赋能乳腺外科精准决策与全周期病人管理[J].外科理论与实践,2026,31(3):185-190,6.基金项目
上海体育科技项目(26Q013) （26Q013）
上海市卫健委医学新技术研究与转化种子计划重点项目(2025ZZ1009) （2025ZZ1009）
上海市卫健委智慧医疗专项(2025ZHYL003) （2025ZHYL003）
国家自然科学基金(82573726) （82573726）
