Generative artificial intelligence applied to mental health: a bibliometric analysis
DOI:
https://doi.org/10.61347/rcss.v1i2.e9Keywords:
Bibliometric analysis, GenAI, generative artificial intelligence, LLM, mental healthAbstract
Introduction: generative artificial intelligence has emerged as a disruptive technology with significant potential to transform research and practice in mental health, fostering new approaches to analysis, diagnosis, and therapeutic support.
Objective: to characterize the scientific production on generative artificial intelligence in mental health through a bibliometric analysis, identifying temporal trends, collaboration patterns, and major emerging themes.
Methods: a quantitative and descriptive bibliometric study was conducted based on documents indexed in the Scopus database, retrieved using a structured search strategy and clearly defined eligibility criteria. The final corpus comprised 1,988 studies published between 2020 and 2025, which were analyzed using the bibliometrix package and its Biblioshiny interface to perform performance analysis and scientific mapping.
Results: an exponential growth in scientific output has been observed since 2023, accompanied by a high level of international collaboration and a strong concentration of publications in leading countries and institutions from the United States and China. Thematically, large language models and conversational systems have consolidated as central axes of the field, while emerging research perspectives are linked to computational psychiatry, ethics, and digital mental health.
Conclusions: GenAI is positioned as a structural component of the scientific ecosystem in mental health, shaping a paradigm shift that calls for interdisciplinary approaches, robust ethical frameworks, and a research agenda oriented toward its responsible and sustainable integration.
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