在心理健康实践中整合大型语言模型:基于专家采访的定性描述性研究
Yingzhuo Ma1, Yi Zeng1, Tong Liu1
1Department of Nursing, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Frontiers in public health
|November 19, 2024
概括
专家们探索了将大型语言模型 (LLM) 整合到心理健康护理中. 调查结果强调了潜在的实践改革和风险,强调了有效实施LLM的培训,指导方针和透明度的需要.
科学领域:
- 医疗保健中的人工智能
- 心理健康技术整合 心理健康技术整合
- 临床实践创新 临床实践创新
背景情况:
- 对像ChatGPT这样的大型语言模型 (LLM) 越来越感兴趣,用于心理健康应用.
- 关于专家观点的有限报告,关于LLM在心理健康实践中的整合.
- 需要了解心理健康中LLM的概念化和感知到的好处/挑战.
研究的目的:
- 探索心理健康和人工智能专家对LLMs的概念化.
- 调查专家对将LLMs纳入心理健康实践的看法.
- 确定在心理健康领域采用LLM的潜在好处,风险和先决条件.
主要方法:
- 在线半结构面试与中国的21名专家 (精神病医生,护士,人工智能研究人员) 进行 (2024年2月至4月).
- 用于招聘专家的雪球和有目的的抽样.
- 传统的内容分析应用于对LLM整合前景的采访数据.
主要成果:
- 出现了四个主题:实践/应用改革 (获取,参与,效率),技术和心理健康差距 (错误信息,缺乏细微差别,用户风险).
- 确定了整合的先决条件:培训,准则,患者参与和透明度.
- 专家的期望包括合理的工作量分配和LLM升级.
结论:
- 调查结果为心理健康领域的LLM有效实施,管理和优化提供了关键指导.
- 成功的整合可以提高心理健康服务的质量和可访问性.
- 解决已识别的风险和先决条件对于负责任的LLM采用至关重要.
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