评估ChatGPT在预测药物相互作用方面的能力:使用住院患者数据的现实世界证据
Ramya Padmavathy Radha Krishnan1, Euniss Hinyo Hung1,2, Megan Ashford2
1Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia.
British journal of clinical pharmacology
|October 3, 2024
概括
在住院患者中,ChatGPT努力准确预测药物相互作用 (DDI). 在生成人工智能能够在临床实践中可靠地评估潜在的DDI之前,需要进一步开发.
科学领域:
- 药理学和人工智能的人工智能
- 临床信息学 临床信息学
背景情况:
- 药物相互作用 (DDI) 对健康构成重大负担.
- 临床医生的时间限制和患者的低健康素养加剧了DDI管理的挑战.
研究的目的:
- 评估ChatGPT (生成人工智能) 在预测DDI方面的诊断准确性.
- 将ChatGPT的DDI预测与专家药剂师在现实环境中的评估进行比较.
主要方法:
- 使用标准化提示来输入患者数据 (人口统计,诊断,药物) 进入ChatGPT版本3.5.
- 将ChatGPT的DDI预测与药剂师的评估进行了比较.
- 使用接收器操作特征曲线和评分器间可靠性 (科恩和弗莱斯的卡帕) 计算的诊断准确性.
主要成果:
- 根据提示的措辞,ChatGPT的DDI预测准确度有所不同,当"药物相互作用"被提及时,敏感度更高.
- 混矩阵显示了低的真正阳性和高的真负率.
- 在ChatGPT和药剂师之间观察到最少的协议 (科恩的kappa:0.077-0.143).
结论:
- 在识别潜在的DDI时,ChatGPT的敏感性较低.
- 目前的生成人工智能模型需要进一步开发,以进行可靠的现实世界DDI评估.
- 人工智能工具还没有准备好取代专家临床判断来评估DDI.
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