大型语言模型在癌症决策中的整合:系统性审查和元分析
Yuexing Hao1,2,3, Zhiwen Qiu4, Jason Holmes5
1Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ, USA. yh727@cornell.edu.
NPJ digital medicine
|July 17, 2025
概括
大型语言模型 (LLM) 在瘤学决策方面表现有前途,但目前的准确率和评估方法突出显著的差距. 需要进一步的研究来确保它们的安全和可靠的临床整合.
科学领域:
- 在瘤学瘤学.
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 大型语言模型 (LLM) 越来越多地用于帮助癌症患者和临床医生在医学决策过程中.
- 在瘤学领域,LLM的整合和评估是一个快速发展的研究领域.
研究的目的:
- 系统地审查LLM在瘤实践中的整合.
- 分析研究人员如何评估LLMs在癌症护理中的性能和实用性.
主要方法:
- 在主要数据库 (PubMed,科学网,Scopus,ACM数字图书馆) 进行了全面的文献搜索,截至2024年5月.
- 包括的研究涵盖了15种不同类型的癌症,其中56项研究被选择用于分析.
- 使用元分析来综合LLM绩效的定量发现.
主要成果:
- 在瘤学中,LLM经常用于总结,翻译和传达临床信息.
- LLM的平均整体准确率为76.2%,诊断准确率平均低于67.4%.
- 评估主要使用量化数据和自动化指标,重点关注准确性和适当性,同时忽视安全性,危害性和清晰性.
结论:
- 目前的LLM绩效和评估方法表明,癌症决策中的临床准备能力有限.
- 关键的挑战包括有限的领域知识和人类监督的必要性.
- 标准化的评估框架和开放的数据集对于提高瘤学LLM可靠性至关重要.
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