EvoMDT:在多种癌症中进行结构化临床决策的自我发展的多剂系统
Qicai Liu1,2, Zhichao Hu3, Tao Huang4
1Department of Reproductive Medicine Centre, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
NPJ digital medicine
|January 7, 2026
概括
通过使用人工智能来改善多学科瘤委员会 (MDT) 的决策,EvoMDT增强了癌症护理. 这种适应性系统提供了与证据相关的建议,提高了瘤实践的准确性和效率.
科学领域:
- 在瘤学瘤学.
- 人工智能的人工智能
- 临床决策支持 临床决策支持
背景情况:
- 多学科瘤委员会 (MDT) 对于癌症护理至关重要,但在专家可用性和一致的决策质量方面面临挑战.
- 随着临床证据的不断发展和工作负载的增加,需要适应性和可审计性AI驱动的决策支持系统.
研究的目的:
- 引入EvoMDT,这是一个新的AI框架,旨在提高MDT决策的稳定性,可追溯性和效率.
- 评估EvoMDT与领先的大型语言模型 (LLM) 和人类MDT在瘤学环境中的性能.
主要方法:
- 基于专家反和结果信号,EvoMDT使用自进化的循环来动态更新提示,共识权重和检索范围.
- 该系统使用特定领域的代理来推断损伤级数据,加上结构化知识检索和与证据相关推的共识协议.
- 使用公共瘤学QA基准,现实世界数据集 (乳腺,肝脏,肺,淋巴瘤) 和单盲医生评估来评估绩效.
主要成果:
- 与前沿的LLM相比,EvoMDT表现优越,与专家计划实现了更高的指导方针一致性和语义对齐 (BERTScore 0.62-0.68).
- 该系统显著减少了安全违规行为,并实现了与人类MDT可比的决策质量,反应时间减少了30-40%.
- 定量指标和医生评估证实了EvoMDT的事实性,准则遵守,临床适当性和可用性.
结论:
- EvoMDT提供了一个可解释和可追踪证据的AI框架,用于在多学科瘤实践中运行推理.
- 该系统为可靠的,病变级精确的癌症护理提供了可扩展的基础,解决了当前MDT过程的局限性.
- EvoMDT代表了人工智能辅助的复杂癌症管理临床决策支持的重大进步.
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