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Abstract

Artificial Intelligence (AI) has emerged as a popular focus in scientific research, yet research on applying AI in the mental health field needs consolidation of existing data on its application efficiency. This bibliometric study aims to consolidate existing knowledge and explore scientific patterns related to AI’s role in mental health. It examines how the advancements of modern technology, including machine learning, chatbots, and robots, are used to predict, prevent, and treat mental disorders, and measure its effectiveness. A literature search was conducted using LENS software, analysing peer-reviewed empirical studies in English from Scopus and Web of Science databases (2004–2024). Reviews, conference papers, dissertations, and protocols were excluded. Sixty-eight relevant publications were identified and analysed for patterns, trends, and associations. Findings show a sharp rise in publications post-2020, likely driven by the COVID-19 pandemic. Clinical and applied psychology are the dominant fields. The University of Washington and Sapienza University of Rome emerged as top institutions. Eating and Weight Disorders and Mindfulness were the most cited journals. AI shows promise in detecting depression and suicidal ideation, preventing mental health disorders, and moderately enhancing psychological interventions - especially when used alongside traditional therapy. However, gaps remain, including limited research on diverse populations and unclear factors influencing user engagement and acceptance of AI tools. This study provides researchers with an overview of publication trends and highlights areas for future investigation. It also supports practitioners in selecting appropriate AI-enhanced interventions to improve outcomes and well-being in mental health care.