探索心理健康支持的大型语言模型的可信性:范围审查协议
Dipak Gautam1, Philipp Kellmeyer2,3,4
1School of Business Informatics and Mathematics, University of Manneim, Mannheim, Germany.
JMIR research protocols
|January 29, 2025
概括
本范围审查检查了大语言模型 (LLMs) 在心理健康支持中的可信性. 它确定了影响LLM可靠性,可解释性和负责任整合道德的因素.
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
- 自然语言处理自然语言处理.
背景情况:
- 大型语言模型 (LLM),包括来自变压器的双向编码器表示 (BERT) 和生成预训练变压器 (GPT),代表了自然语言处理的重大进步.
- 这些人工智能技术越来越多地被整合到心理健康支持应用中.
- 在心理健康背景下,LLM的可信性,可靠性和可解释性需要进一步调查.
研究的目的:
- 系统地绘制影响LLM在心理健康支持中的可信度的因素.
- 分析心理健康应用LLM的可靠性,可解释性和伦理考虑.
- 为从业者,研究人员和政策制定者提供有关负责任的LLM整合的见解.
主要方法:
- 范围审查遵循PRISMA-ScR和乔安娜·布里格斯研究所 (JBI) 的方法.
- 资格标准包括在心理健康支持中使用基于变压器的生成语言模型 (例如BERT,GPT) 的研究.
- 从2019年到2024年10月,对主要数据库 (PsycINFO,MEDLINE,Web of Science,IEEE Xplore,ACM数字图书馆) 进行系统搜索,并进行定性数据合成.
主要成果:
- 系统搜索已经完成,截至2024年9月,选阶段正在进行中.
- 数据提取预计将于2024年11月初完成,合成预计将于2024年11月下旬完成.
- 目前的进展表明正在进行审查过程.
结论:
- 这次审查将绘制现有证据,以确定LLM在心理健康方面的可信度.
- 它将确定影响LLM可靠性,可解释性和伦理影响的关键因素.
- 结果将为未来的研究,政策和实践提供信息,以确保在心理健康服务中安全部署LLM.
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