一个认知层架构,以支持心理治疗交互中的大语言模型性能.
Max Rollwage1, Jessica McFadyen2, Keno Juchems2
1Limbic, London, UK. rollwagm@gmail.com.
Nature medicine
|March 13, 2026
概括
具有专门的认知层架构的大型语言模型 (LLM) 在心理健康护理中显示出前景. 这些人工智能代理在提供认知行为疗法,改善患者福祉方面,超过了标准的LLM和人类治疗师.
科学领域:
- 医疗保健中的人工智能
- 临床心理学 临床心理学
- 自然语言处理自然语言处理.
背景情况:
- 临床医生与患者之间的互动是精神卫生保健的核心.
- 大型语言模型 (LLM) 在面向患者的心理健康支持中的有效性基本上没有得到证实.
- 一般目的的LLM缺乏专门的心理治疗推理能力.
研究的目的:
- 引入和评估一种认知层架构,以临床心理治疗推理增强LLM.
- 评估增强型LLM与独立的LLM和人类临床医生在心理健康课程中的表现.
- 调查基于LLM的AI疗法的现实有效性和临床影响.
主要方法:
- 一个随机的双盲评估,涉及227名参与者与治疗药物相互作用,以生成心理健康会议成绩单.
- 由22名专家临床医生根据关键认知行为治疗能力对成绩单的评估.
- 从一个大规模的现实世界部署中分析了19674个成绩单,支持8920名用户.
主要成果:
- 用认知层架构增强的LLM在提供认知行为疗法方面始终优于独立的LLM和人类临床医生.
- 在现实世界的部署中,增加的认知层激活与更大的症状改善相关.
- 更高的认知层激活与长期临床康复 (大约10周) 的可能性增加有关.
结论:
- 认知层架构可以为LLM提供高质量的认知行为疗法交互.
- 人工智能辅助疗法显示出增强精神卫生保健服务的潜力.
- 需要进一步的研究来探索人工智能的机制和临床疗效,以支持心理健康.
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