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ORIGINAL RESEARCH article

Front. Public Health
Sec. Public Mental Health
Volume 12 - 2024 | doi: 10.3389/fpubh.2024.1475867

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

Provisionally accepted
Yingzhuo Ma Yingzhuo Ma Yi Zeng Yi Zeng TONG LIU TONG LIU Ruoshan Sun Ruoshan Sun Jun Wang Jun Wang Mingzhao Xiao Mingzhao Xiao *
  • First Affiliated Hospital of Chongqing Medical University, Chongqing, China

The final, formatted version of the article will be published soon.

    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.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 reforms 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 of LLMs integration in mental health (training and competence, guidelines for use and management, patient engagement and transparency) and expressed their (4) expectations for future developments (reasonable allocation of workload, upgrades and revamps of LLMs).These findings provide valuable insights on integrating LLMs within mental health practice, offering critical guidance for institutions to effectively implement, manage, and optimize these tools, thereby enhancing the quality and accessibility of mental health services.

    Keywords: Large Language Model1, Qualitative study2, ChatGPT3, Public Health, Mental Health

    Received: 04 Aug 2024; Accepted: 15 Oct 2024.

    Copyright: © 2024 Ma, Zeng, LIU, Sun, Wang and Xiao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Mingzhao Xiao, First Affiliated Hospital of Chongqing Medical University, Chongqing, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.