JMIR Publications

ABSTRACT

Background:

Mental disorders have ranked among the top ten prevalent causes of burden on a global scale. Generative AI (GAI) emerges as a promising and innovative technological advancement that has significant potential in the field of mental health care. Nevertheless, there is a scarcity of research dedicated to examining and understanding the application landscape of GAI within this domain.

Objective:

This review aims to inform the current state of GAI knowledge and identify its key use in the mental health domain by consolidating relevant literature.

Methods:

Records were searched within eight reputable sources including Web of Science, PubMED, IEEE Xplore, MedRxiv, BioRxiv, Google Scholar, CNKI and Wanfang databases between 2013 and 2023. For an exhaustive search, we also checked the studies cited by relevant literature. Two reviewers were responsible for the data selection process and all the extracted data was synthesized and summarized for brief and in-depth analyses depending on the GAI approaches used (traditional retrieval/rule-based techniques v.s. advanced GAI techniques).

Results:

In this review of 145 articles, 44 met inclusion criteria for detailed analysis. Six key uses of advanced GAI emerged: mental disorder detection, counseling support, therapeutic assistance, clinical training, clinical decision-making support, and goal-driven optimization. Advanced GAI systems have been mainly utilized in therapeutic assistance (n=19, 43%) and counseling support (n=13, 30%), with clinical training being the least common. Most studies (n=28, 64%) focused broadly on mental health, while specific conditions such as anxiety, bipolar disorder, eating disorders, PTSD, and schizophrenia received limited attention (n=1, 2%). Despite prevalent use, the efficacy of ChatGPT in the detection of mental disorder remains insufficient. Additionally, 101 articles on traditional GAI approaches were found, indicating diverse areas where advanced GAI could enhance mental health care.

Conclusions:

This study provides a comprehensive overview of the utilization of GAI in mental health care, which serve as a valuable guide for future research, practical applications, and policy development in this domain. While GAI demonstrates promise in augmenting mental health care services, its inherent limitations emphasize its role as a supplementary tool rather than a replacement for trained mental health providers. A conscientious and ethical integration of GAI techniques is necessary, ensuring a balanced approach that maximizes benefits while mitigating potential challenges in mental health care practices.