在LLM驱动的CDSS中对负责任的AI进行合作框架,用于精密瘤学,利用真实世界的患者数据
Sonja Mathes1,2, Dyke Ferber3,4,5, Tobias Dreyer6,7
1TUM School of Medicine and Health, Department of Dermatology, Technical University of Munich, Munich, Germany. sonja.mathes@mri.tum.de.
NPJ precision oncology
|December 4, 2025
概括
大型语言模型 (LLM) 可以为精确瘤学构建现实世界的数据. 一个新的框架指导负责任的LLM集成,解决数据瓶和推动生物标志物发现.
科学领域:
- 在瘤学瘤学.
- 生物医学信息学 生物医学信息学
- 人工智能的人工智能
背景情况:
- 精确瘤学依赖于现实世界的数据来识别生物标志物和治疗方法.
- 非结构化数据在当前精确瘤学实践中存在重大瓶.
- 大型语言模型 (LLM) 为数据结构化挑战提供了潜在的解决方案.
研究的目的:
- 提出一个框架,以负责任地将LLMs纳入精密瘤学.
- 解决目前精密瘤学研究和应用中的与数据相关的瓶.
- 引导多学科专家利用LLM来提高精确的癌症护理.
主要方法:
- 制定一个负责任的LLM整合框架,由多学科专家共同创建.
- 为LLM实施建立五个主题维度和十个指导原则.
- 该框架应用于涉及子宫癌肉瘤数据的思想实验.
主要成果:
- 拟议的框架为在精密瘤学中利用LLMs提供了一个结构化的方法.
- 该框架解决了处理非结构化真实世界数据的关键挑战.
- 插图应用证明了框架在特定癌症类型中的实用性.
结论:
- 负责任的LLM整合对于推进精密瘤学至关重要.
- 该框架促进有效使用LLM用于生物标志物发现和治疗识别.
- 这种方法支持克服数据瓶,并通过AI增强癌症护理.
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