JMIR Publications

ABSTRACT

Background:

As mental health challenges continue to rise globally, there is increasing interest in the use of generative pre-trained transformers (GPT) models, such as ChatGPT, in mental healthcare. A few months after its release, tens of thousands of users interacted with GPT-based therapy bots ,with mental health support identified as the primary use case. ChatGPT offers scalable and immediate support through natural language processing capabilities, but their clinical applicability, safety, and effectiveness remain underexplored.

Objective:

This scoping review aims to provide a comprehensive overview of the main clinical applications of ChatGPT in mental healthcare, along with the existing empirical evidence for its performance.

Methods:

A systematic search was conducted in 8 electronic databases through April 2025 to identify primary studies. Eligible studies included primary research, reporting on the evaluation of a ChatGPT clinical application implemented for a mental healthcare–specific purpose.

Results:

Sixty studies were included in this scoping review. The results highlighted that most applications used generic ChatGPT and focused on the detection of mental health problems and counselling and treatment, while only a minority of studies investigated ChatGPT use in clinical decision facilitation and prognosis tasks. Most of the studies were prompt experiments, in which standardized text inputs—designed to mimic clinical scenarios, patient descriptions, or practitioner queries—are submitted to ChatGPT to evaluate its performance in mental health-related tasks. In terms of performance, ChatGPT shows good accuracy in detecting specific mental health problems, simulating therapeutic conversation, providing psychoeducation, and conducting specific therapeutic strategies. However, ChatGPT has significant limitations with more complex clinical presentations and its overly pessimistic prognostic outputs. Nevertheless, overall, when compared to mental health experts or other artificial intelligence (AI) models, ChatGPT approximates or surpasses their performance in conducting various clinical tasks. Finally, custom ChatGPT use was associated with better performance, especially in counselling and treatment tasks.

Conclusions:

While ChatGPT offers promising capabilities for mental health screening, psychoeducation, and structured therapeutic interactions, its current limitations highlight the need for caution in clinical adoption. These limitations also underscore the need for rigorous evaluation frameworks, model refinement, and safety protocols before broader clinical integration. Moreover, the variability in performance across versions, tasks and diagnostic categories also invites a more nuanced reflection on the conditions under which ChatGPT can be safely and effectively integrated into mental health settings. Clinical Trial: The protocol for scoping review was registered on Open Science Framework https://osf.io/z6kyg