大型语言模型在应对抑郁症时的安全性
1Medical Education and Clinical Sciences, Washington State University, Spokane, USA.
Cureus
|December 19, 2023
概括
生成性AI心理健康聊天机器人识别和升级严重抑郁和自杀风险很慢,可能会推迟关键的人类干预. 在临床使用之前,进一步测试至关重要.
科学领域:
- 心理健康技术 心理健康技术
- 人工智能安全问题 人工智能安全问题
- 临床信息学 临床信息学
背景情况:
- 像ChatGPT这样的生成人工智能模型显示出对心理健康支持的希望.
- 心理健康聊天机器人使用预先结构化的提示来进行用户交互.
- 人工智能能检测严重抑郁和自杀倾向的能力存在担忧.
研究的目的:
- 评估公开可用的ChatGPT-3.5对话代理的安全性.
- 为了评估AI对模拟恶化的抑郁症和自杀倾向的反应.
- 为了确定AI代理人是否可以安全地处理心理健康危机场景.
主要方法:
- 从FlowGPT.com.com对25个ChatGPT-3.5代理进行了评估.
- 使用了两个结构化的患者模拟:一个是升级PHQ-9分数的,一个是概括的.
- 记录了当代理人引用人类支持和当他们关闭时.
主要成果:
- 人工智能代理人在模拟中期左右提及人类支持,PHQ-9分数为12 (中度抑郁症).
- 最终关闭主要发生在严重抑郁水平 (PHQ-9分数为25).
- 很少有代理商 (2/25) 提供危机资源;22/25 在降低风险后恢复对话.
结论:
- 目前的人工智能聊天机器人表现出推迟升级的心理健康风险,造成潜在的危险.
- 在临床部署AI心理健康工具之前,严格的测试和监督是必不可少的.
- 需要进一步研究持续参与效应和人工智能可访问性风险效益分析.
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