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心理健康应用的大型语言模型:系统审查

Zhijun Guo¹, Alvina Lai¹, Johan H Thygesen¹

¹Institute of Health Informatics University College, London, London, United Kingdom.

JMIR mental health

|October 18, 2024

概括

大型语言模型 (LLM) 在心理健康查和干预方面表现有前途,但目前的风险,如不准确性和道德问题,超过了临床使用的好处. 需要进一步的研究来开发LLM作为安全的临床辅助工具.

科学领域:

人工智能在医学中的应用
数字心理健康数字心理健康
临床信息学临床信息学

背景情况:

大型语言模型 (LLM) 是先进的人工智能,在数字健康领域具有潜力.
它们在心理健康临床环境中的应用仍在争论中.
本综述批判性地评估了LLM在心理健康中的应用.

研究的目的:

系统地审查LLM在心理健康中的适用性和有效性.
专注于早期查,数字干预和临床环境.
分析模型,方法,数据和结果,以突出潜力和挑战.

主要方法:

按照PRISMA指南进行系统审查.
在 MEDLINE,IEEE Xplore,Scopus,JMIR 和 ACM 数字图书馆进行了搜索.
包括2017年1月至2024年4月的英语文章,使用关键词 (心理健康或精神疾病或精神障碍或精神病学) 和 (大型语言模型).

主要成果:

评估了40篇文章:38%用于条件检测,18%用于对话代理,45%用于其他应用.
在检测心理健康问题和提供可访问的电子健康服务方面,LLM 已经证明了其有效性.
当前的临床风险 (不一致性,幻觉,缺乏道德框架) 可能超过益处.

结论:

在心理健康方面,LLM具有临床辅助的潜力,但不能替代专业服务.
确定的问题包括缺乏多语言专家注释的数据集,准确性问题,可解释性挑战和道德困境.
持续的研究对于负责任地为心理健康应用开发LLM至关重要.

关键词:

贝尔特 (BERT) 公司来自变压器的双向编码器表示聊天GPT 聊天GPT 聊天数字医疗保健数字医疗保健大型语言模型.心理健康心理健康

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