Large Language Models in Mental Health Care: a Scoping Review
Yining Hua Fenglin Liu Kailai Yang Zehan Li Yi-han Sheu Peilin Zhou Lauren V. Moran Sophia Ananiadou Andrew Beam
Objective: The growing use of large language models (LLMs) stimulates a need
for a comprehensive review of their applications and outcomes in mental health
care contexts. This scoping review aims to critically analyze the existing
development and applications of LLMs in mental health care, highlighting their
successes and identifying their challenges and limitations in these specialized
fields. Materials and Methods: A broad literature search was conducted in
November 2023 using six databases (PubMed, Web of Science, Google Scholar,
arXiv, medRxiv, and PsyArXiv) following the 2020 version of the Preferred
Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A
total of 313 publications were initially identified, and after applying the
study inclusion criteria, 34 publications were selected for the final review.
Results: We identified diverse applications of LLMs in mental health care,
including diagnosis, therapy, patient engagement enhancement, etc. Key
challenges include data availability and reliability, nuanced handling of
mental states, and effective evaluation methods. Despite successes in accuracy
and accessibility improvement, gaps in clinical applicability and ethical
considerations were evident, pointing to the need for robust data, standardized
evaluations, and interdisciplinary collaboration. Conclusion: LLMs show
promising potential in advancing mental health care, with applications in
diagnostics, and patient support. Continued advancements depend on
collaborative, multidisciplinary efforts focused on framework enhancement,
rigorous dataset development, technological refinement, and ethical integration
to ensure the effective and safe application of LLMs in mental health care.