基于大型语言模型的聊天机器人和心理健康咨询的代理人工智能:对方法,评估框架和道德保障的系统审查
Ha Na Cho1, Kai Zheng2, Jiayuan Wang2
1University of California, Irvine, 5029 Donald Bren Hall, Irvine, US.
JMIR AI
|January 27, 2026
概括
大型语言模型 (LLM) 聊天机器人显示出对心理健康 (MH) 咨询的希望. 然而,有限的外部验证和不一致的安全和治理实践报告阻碍了临床准备.
科学领域:
- 数字心理健康 (MH) 是指数字心理健康.
- 交谈式的人工智能 (AI)
背景情况:
- 基于大型语言模型 (LLM) 的聊天机器人正在成为数字心理健康 (MH) 咨询的工具.
- 关于它们的方法质量,评估严格性和道德保障的证据是分散的,影响了临床准备和部署安全.
研究的目的:
- 系统地审查基于LLM的MH咨询聊天机器人的方法,评估实践和伦理/治理框架.
- 识别影响这些工具的有效性,可复制性和翻译性的差距.
主要方法:
- 搜索了主要的学术数据库 (谷歌学者,PubMed,IEEE Xplore,ACM数字图书馆) 来查找2020年1月至2025年5月的研究.
- 包括LLM驱动的MH咨询聊天机器人的研究,不包括那些没有LLM代理或可评估的输出的人.
- 通过交通灯框架评估研究质量,并通过叙事合成合成数据.
主要成果:
- 包括20项研究,其中基于GPT的模型在45%使用,精细调整的模型在90%使用.
- 评估经常将定性评估与定量语言指标相结合.
- 在外部验证和伦理/安全报告中发现了局限性;没有研究报告注册的随机对照试验或独立的临床验证.
结论:
- 基于LLM的MH聊天机器人提供了可扩展,个性化的支持的潜力,但需要更严格的评估.
- 未来的研究必须侧重于临床基础的框架,透明的报告和强大的安全部署验证.
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