大型语言模型能否检测到导致药物不良反应的药物相互作用?
Justine Sicard1, François Montastruc1, Coline Achalme1
1Department of Medical and Clinical Pharmacology, Centre of PharmacoVigilance and Pharmacoepidemiology, Faculty of Medicine, Toulouse University Hospital (CHU), Toulouse, France.
Therapeutic advances in drug safety
|May 19, 2025
概括
大型语言模型 (LLM) 可以在药物不良反应 (ADR) 报告中检测药物相互作用 (DDI),ChatGPT和Claude的准确性高于Gemini. 然而,在没有相互作用的情况下,LLM很难排除DDI,需要人类专家的审查.
科学领域:
- 药物监督 药物监督 药物监督
- 人工智能的人工智能
- 药品安全 药品安全
背景情况:
- 药物相互作用 (DDI) 是药物不良反应 (ADR) 的重要原因之一.
- 评估大型语言模型 (LLM) 的潜力,以帮助药监专家识别与ADR相关的DDI,对于提高药物安全至关重要.
研究的目的:
- 评估和比较三个领先的LLM (ChatGPT,Gemini和Claude) 在检测和解释导致ADR通知的临床显著DDI方面的表现.
主要方法:
- 一项使用法国国家药监数据库的观察性,横截面研究.
- 从2022年开始的个体病例安全报告 (ICSR),包括带有DDI (阳性对照) 和没有DDI (负面对照) 的个体病例安全报告,被随机抽取.
- 要求LLM提供病例摘要,并通过敏感性,特异性和F-测量来评估他们检测DDI的能力,并根据困难程度分类病例.
主要成果:
- 聊天GPT实现了99%的DDI检测,其次是克劳德和双子座95%.
- 与Gemini (33%) 相比,ChatGPT和Claude在正确识别DDI方面表现出更高的准确性 (分别为66%和68%),ChatGPT/Claude的F值为0.83-0.85,而Gemini的F值为0.63-0.68.
- 所有的LLM都表现出低特异性,在负控案例中错误地识别了DDI.
结论:
- 在检测与药物监测报告相关的DDI方面,LLM显示出前景,但缺乏在非相互作用情况下排除DDI的可靠性.
- 药理学家对于准确确定DDI在ADR中的作用至关重要.
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