评估生成AI聊天机器人对滥用酒精的支持:一个纵向模拟研究
Lori Uscher-Pines1, Jessica L Sousa2, Pushpa Raja3
1RAND Corporation, Arlington, Virginia.
NEJM AI
|January 26, 2026
概括
大型语言模型 (LLM) 聊天机器人显示出对行为健康支持的承诺,特别是在同情心方面. 然而,他们关于滥用酒精的建议需要提高信息质量和准确性.
科学领域:
- 数字健康数字健康
- 医疗保健中的人工智能
- 心理健康技术 心理健康技术
背景情况:
- 大型语言模型 (LLM) 聊天机器人越来越多地用于行为健康支持.
- 对他们关于滥用酒精的建议的严格评估是有限的.
研究的目的:
- 评估公开可用的聊天机器人的性能,以应对滥用酒精的场景.
- 评估跨领域的聊天机器人交互,包括同情心,信息质量和有用性.
主要方法:
- 七个聊天机器人 (以一般和行为健康为重点) 通过在七天内模拟的纵向交互进行了测试.
- 临床医生根据同情心,信息质量,有用性,响应性和范围意识来评价成绩单.
- 二次评估包括污名化语言和验证与挑战用户.
主要成果:
- 共感是评分最高的领域 (平均得分4.6/5),而信息质量是最低的 (平均2.7/5).
- 聊天机器人的性能差异很大,一般和行为健康类型之间没有差异.
- 所有的聊天机器人都提供了一些不恰当的,夸张的或不准确的指导,尽管避免了污名化的语言.
结论:
- 聊天机器人在同情心方面表现出强项,但需要在提供酒精滥用支持的信息质量和准确性方面显著改进.
- 用户和临床医生应该意识到目前在这个领域的聊天机器人的局限性.
- 需要进一步开发以提高人工智能驱动的行为健康工具的可靠性.
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