JMIR Publications

ABSTRACT

Background:

Large language models (LLMs) are poised to transform mental healthcare, offering advanced capabilities in diagnosis, prognosis, and decision support. Since their inception, numerous mental health-focused LLMs have emerged in scientific literature, reflecting the growing interest in leveraging these models across various clinical applications. With a broad range of models available, diverse optimization strategies, and multiple use cases, reviewing the current landscape is critical to understanding where future impact lies.

Objective:

We performed a scoping review investigating the use of LLMs in mental health across diagnostic, prognostic, and decision support tasks.

Methods:

We screened 3,121 papers from PubMed, Scopus, and Web of Science for studies published between January 2023 and October 2025, using terms related to LLM and mental health. After removing duplicates, two reviewers independently screened the studies, with a third to resolve conflicting opinions. We extracted and synthesized information on the models, use-case, datasets and adaptation methods from selected papers.

Results:

41 papers were selected. Many studies included evaluations on OpenAI’s GPT series applications: GPT-4 (24 studies, 58.5%) and GPT-3.5 (15 studies, 36.6%). Others included BERT derived models (7 studies, 17.1%), LLaMA (8 studies, 19.5%), and RoBERTa derived models (6 studies, 14.6%). While all studies initially applied out-of-box LLMs, several adapted them through few-shot learning or fine-tuning to better align with specific research goals. The most common use case was in diagnostics (31 studies, 75.6%) while the most common target condition was depression (11 studies, 26.8%). While many studies reported superior performance of LLMs, only a minority of studies (13 studies, 29.3%) validated LLM performance against clinician assessments using real patient data, with the majority relying on proxy outcomes such as clinical vignettes, exam questions, or social media posts.

Conclusions:

Despite rapid growth and diversity of LLM applications in mental health, the field remains nascent and exploratory. Future developments must emphasize consistent model adaptation procedures to ensure safety and clinical workflow alignment. Models must also be evaluated on robust evaluation criteria, using standardized protocols and real clinical outcome measures.