Rx-LLM:一个基准测试套件,用于评估安全的大型语言模型对药物相关任务的性能
Xingmeng Zhao1, Kaitlin Blotske1, Moriah Cargile2
1University of Colorado School of Medicine, Department of Biomedical Informatics.
medRxiv : the preprint server for health sciences
|December 17, 2025
概括
这项研究评估了关于综合药物管理 (CMM) 任务的大型语言模型 (LLM). LLaMA3-70B在四个领域表现出色,而GPT-4o-mini在药物相互作用和脏剂量方面表现出色.
科学领域:
- 医疗保健中的人工智能
- 临床信息学 临床信息学
- 药理学 药理学是指药理学的学科.
背景情况:
- 大型语言模型 (LLM) 需要专门的基准来安全管理药物.
- 综合药物管理 (CMM) 是药物优化的标准.
- 目前的LLM需要对临床药物任务进行评估.
研究的目的:
- 为LLM评估设计CMM一致的基准.
- 量化领先的LLM在药物相关任务的基线绩效.
主要方法:
- 开发了六个CMM过程的基准:配方,订单生成,途径,药物相互作用,脏剂量和指示匹配.
- 每个基准创建了250个临床医生注释的数据对.
- 评估了GPT-4o-mini,MedGemma-27B和LLaMA3-70B,使用零射击提示和特定任务的指标.
主要成果:
- 在配方,订单生成,路线和指示匹配方面,LLaMA3-70B领先.
- 在药物相互作用识别和脏剂量调整方面,GPT-4o-mini获得了最高的准确性.
- 在不同模型和任务中,一致性差异很大.
结论:
- 在与药物相关的任务中,LLM的表现有很大差异.
- LLaMA3-70B在制定,订购,路线和指示任务方面表现有前途.
- GPT-4o-mini显示了药物相互作用和脏剂量调整的潜力.
- 具体任务评估对于临床决策支持LLM部署至关重要.
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