聊天GPT与临床决策支持系统在药物相互作用的分析中
Thorsten Bischof1, Valentin Al Jalali1, Markus Zeitlinger1
1Department of Clinical Pharmacology, Medical University of Vienna, Vienna, Austria.
Clinical pharmacology and therapeutics
|February 12, 2025
概括
像Chat GPT这样的大型语言模型显示了分析药物副作用的潜力,但目前无法可靠地检测潜在的药物相互作用 (pDDI). 在用于pDDI分析的临床使用之前,需要进一步开发.
科学领域:
- 药理学 药理学是指药理学的学科.
- 人工智能的人工智能
- 临床信息学 临床信息学
背景情况:
- 目前的潜在药物相互作用 (pDDI) 分析是资源密集型的,涉及多个临床决策支持系统 (CDSS) 和专家解释.
- 人工智能 (AI) 和大型语言模型 (LLM) 的出现为简化pDDI分析提供了潜在的替代方案.
研究的目的:
- 为了比较Chat GPT与已建立的CDSS在多药患者中识别临床相关的pDDI的性能.
- 评估通过Chat GPT检测pDDI的准确性,一致性和临床相关性.
主要方法:
- 一项回顾性研究分析了2022年10月至2023年8月期间30名多药患者的pDDI数据.
- 聊天GPT和已建立的CDSS (MediQ®,Lexicomp®,Micromedex®) 被用于pDDI分析.
- 一个多学科团队评估了确定的pDDI的临床相关性和严重程度.
主要成果:
- 已建立的CDSS确定了280个临床相关的pDDI,而Chat GPT只确定了80个.
- 聊天GPT表明pDDI的显著不足检测,特别是那些具有QTc延长风险 (8对85).
- 聊天GPT表现出很高的不一致性 (90%的查询产生了不同的结果),并且未能检测出许多临床相关的相互作用,尽管它为副作用查询提供了良好的建议.
结论:
- 目前使用聊天GPT用于pDDI识别是不推的,因为错过的交互,错误和不一致.
- 虽然Chat GPT对未来药监应用具有前景,但需要大幅改进,以解决其在检测pDDI方面的局限性.
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