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对心理健康护理中生成任务的大型语言模型进行范围审查

Yining Hua^1,2, Hongbin Na³, Zehan Li⁴

¹Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.

NPJ digital medicine

|April 30, 2025

概括

大型语言模型 (LLM) 在心理健康护理中显示出潜力,但目前的证据不足以独立使用. 需要更严格的开发和评估,以确保人工智能在心理健康中的安全临床整合.

科学领域:

人工智能的人工智能
心理健康技术心理健康技术
数字健康数字健康

背景情况:

大型语言模型 (LLM) 提供与心理健康护理相关的人类类对话能力.
在心理健康应用中LLM的临床有效性和安全性需要彻底调查.

研究的目的:

综合当前关于LLM在心理健康中的应用的研究.
为了评估LLM的表现和临床有效性.
识别评估方法的差距,并提出未来的方向.

主要方法:

进行了系统的文献搜索,以确定相关研究.
确定了76个独特的文章,其中16个符合审查的纳入标准.
用一个结构化的评估框架来分析研究方法和发现.

主要成果:

包括的研究探讨了临床援助,咨询,治疗和情绪支持的LLM应用,显示了最初的承诺.
评估方法往往没有标准化,依赖于临时尺度,限制了可比性和稳定性.
对专有模型的大量依赖 (例如,OpenAI的GPT系列) 引发了对透明度和可重复性的担忧.

结论:

目前的证据并不完全支持使用LLM作为心理健康护理的独立干预措施.
标准化的评估框架和严格的开发指南对于LLMs的安全有效的临床整合至关重要.
需要进一步的研究来解决当前评估方法的局限性,并确保AI工具在心理健康环境中的可靠性.

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