Large Language Model for Mental Health: A Systematic Review
Zhijun Guo Alvina Lai Johan Hilge Thygesen Joseph Farrington Thomas Keen Kezhi Li
Large language models (LLMs) have received much attention and shown their
potential in digital health, while their application in mental health is
subject to ongoing debate. This systematic review aims to summarize and
characterize the use of LLMs in mental health by investigating the strengths
and limitations of the latest work in LLMs and discusses the challenges and
opportunities for early screening, digital interventions, and other clinical
applications in mental health. Following PRISMA guidelines, we examined English
articles from PubMed, DBLP Computer Science Bibliography, and IEEE Xplore,
published between 1 January 2017, and 1 September 2023, focusing on mental
health and LLMs. The review analyzed 32 articles, including mental health
analysis using social media datasets (n=13), mental health chatbots (n=10), and
other mental health applications (n=9). Findings reveal LLMs' effectiveness in
mental health issue detection and the enhancement of telepsychological services
through personalised healthcare. Nonetheless, risks like text inconsistencies,
hallucinatory content, and the lack of an ethical framework raise concerns
about their clinical use. Despite these challenges, the advancement of LLMs
underscores their potential as innovative clinical tools, necessitating further
research and development. The review emphasizes that LLMs should complement,
not replace, professional mental health services.