Integrating large language models in mental health practice: a qualitative descriptive study based on expert interviews

作者：Yingzhuo Ma, Yi Zeng, Tong Liu, Ruoshan Sun, Mingzhao Xiao, Jun Wang · 发表于：Frontiers in Public Health · 年份：2024 · DOI：10.3389/fpubh.2024.1475867 · 被引用次数：15 · 研究领域：Artificial Intelligence in Healthcare and Education、Digital Mental Health Interventions、Mental Health via Writing

Background: Progress in developing artificial intelligence (AI) products represented by large language models (LLMs) such as OpenAI's ChatGPT has sparked enthusiasm for their potential use in mental health practice. However, the perspectives on the integration of LLMs within mental health practice remain an underreported topic. Therefore, this study aimed to explore how mental health and AI experts conceptualize LLMs and perceive the use of integrating LLMs into mental health practice. Method: In February-April 2024, online semi-structured interviews were conducted with 21 experts (12 psychiatrists, 7 mental health nurses, 2 researchers in medical artificial intelligence) from four provinces in China, using snowballing and purposive selection sampling. Respondents' discussions about their perspectives and expectations of integrating LLMs in mental health were analyzed with conventional content analysis. Results: Four themes and eleven sub-themes emerged from this study. Firstly, participants discussed the (1) practice and application reform brought by LLMs into mental health (fair access to mental health services, enhancement of patient participation, improvement in work efficiency and quality), and then analyzed the (2) technological-mental health gap (misleading information, lack of professional nuance and depth, user risk). Based on these points, they provided a range of (3) prerequisites for the integration of LLMs in mental health (training and competence, guidelines for...