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科学领域:

  • 人工智能在医学中的应用
  • 临床决策支持系统 临床决策支持系统
  • 医疗信息学 医疗信息学

背景情况:

  • 大型语言模型 (LLM) 越来越多地被评估为医学知识,但它们在临床决策中的实用性,特别是用于工具选择,尚不清楚.
  • 评估LLM推适当医疗计算器的能力对于安全有效的临床实践至关重要.

研究的目的:

  • 评估各种大型语言模型 (LLM) 在推医疗计算器方面的表现,与人类表现相比.
  • 为了确定LLM在选择临床计算器时会犯的错误类型.

主要方法:

  • 九个LLM (开源,专有,域特定) 在1009个选择题中进行了测试,涵盖35个临床计算器.
  • 在100个问题的子集上,LLM的表现与人类注释者进行了比较.
  • 对表现最高的法学士进行了错误分析.

主要成果:

  • 性能最好的LLM在回答有关医疗计算器的问题时达到66.0%的准确率.
  • 人类注释者平均准确率为79.5%,超过了LLM.
  • 在LLM的错误主要是由于理解 (49.3%) 和计算器知识缺陷 (7.1%).

结论:

  • 当前的大型语言模型 (LLM) 在推医疗计算器的临床决策方面并不超过人类的性能.
  • 在临床计算器的选择中,LLM需要在理解和医学知识方面显著改进,才能成为可靠的临床计算器选择工具.