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大型语言模型为患者提出的医学问题提供了不安全的答案

Rachel L Draelos¹, Samina Afreen², Barbara Blasko³

¹Glass Box Medicine, Durham, NC, USA. rachel.draelos@glassboxmedicine.com.

NPJ digital medicine

|February 13, 2026

概括

数百万寻求人工智能聊天机器人的医疗建议的患者面临安全风险. 一项研究发现,聊天机器人安全性存在显著差异,其中一些提供潜在有害的医疗信息.

科学领域:

医疗保健中的人工智能
医疗信息学医疗信息学
患者安全患者安全

背景情况:

大型语言模型 (LLM) 聊天机器人越来越多地被患者用于医疗咨询.
这种广泛使用引发了关于人工智能生成的健康信息的准确性和可靠性的重大患者安全问题.

研究的目的:

为了比较四个领先的公开可用的LLM聊天机器人的临床安全性 (Claude,Gemini,GPT-4o,Llama-3.0/3.1-70B).
通过使用新型数据集和强大的评估框架,评估聊天机器人对寻求医疗咨询问题的反应.

主要方法:

进行了一项由医生领导的红色团队研究.
他们使用了一个名为HealthAdvice的数据集,包括222个患者提出的医疗问题,涉及初级保健,内科,妇女健康和儿科.
总共有888个聊天机器人响应得到了定量和定性分析.

主要成果:

观察到聊天机器人响应安全性的统计学上显著差异.
有问题的回答率在21.6% (克劳德) 到43.2% (拉玛) 之间.
不安全的反应范围从5% (克劳德) 到13% (GPT-4o,Llama),定性分析显示可能对患者造成严重伤害.

结论:

数以百万计的患者可能会从公开可用的AI聊天机器人获得不安全的医疗建议.
进一步的研究和开发对于提高基于LLM的医疗咨询工具的临床安全性和可靠性至关重要.

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