ARTIFICIAL INTELLIGENCE IN MENTAL HEALTH INTERVENTIONS: A BIBLIOMETRIC REVIEW
DOI:
https://doi.org/10.35631/IJEPC.1163035Keywords:
Artificial Intelligence, Mental Health, InterventionAbstract
Artificial intelligence (AI) has developed into a transformative framework within mental health interventions, facilitating the creation of scalable, data-driven, and personalized approaches for diagnosing, monitoring, and treating psychological conditions. Despite the fact that scholarly work in this interdisciplinary field has expanded rapidly, a full and systematic comprehension of its publication trajectories, intellectual architecture, and international collaboration dynamics is still insufficient. This study aims to close the existing gap by undertaking a bibliometric examination of scholarly literature focused on artificial intelligence within mental health interventions, covering the period from 2003 through April 2026. The dataset was compiled from the Scopus database through an advanced search strategy based on three primary keywords, comprising artificial intelligence, AI, and mental health interventions, which yielded a final corpus of 360 documents. A suite of tools, including Scopus Analyzer, OpenRefine, and VOSviewer software, was employed to perform descriptive, performance, and network analyses. Scopus Analyzer was used to evaluate publication trends, subject areas, and country contributions, while OpenRefine ensured data cleaning and consistency. VOSviewer was applied to construct and visualize keyword co-occurrence as well as co-authorship at the country level. The findings demonstrate a marked escalation in scholarly output, particularly after 2023, indicating growing global interest in AI-driven mental health solutions. Keyword analysis underscores prevailing focal areas, including mental health, artificial intelligence, chatbots, machine learning, and depression, while also drawing attention to newer and increasingly prominent subjects such as generative AI, large language models, and digital mental health. In addition, the co-authorship mapping reveals that the United States, the United Kingdom, and Australia function as primary nodes of international research collaboration, playing a pivotal role in connecting global scholarly networks. In summary, this study delivers a thorough examination of the research domain, illustrating that artificial intelligence is substantially reshaping approaches to mental health interventions. The results contribute meaningful insights for both scholars and clinical professionals, while also emphasizing the importance of sustained cross-disciplinary cooperation alongside careful ethical reflection in the progression of AI-driven mental health solutions.
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