Mapping the scientific landscape of artificial intelligence in mental health

Abstract

Artificial Intelligence (AI) has gained increasing popularity in contemporary scientific research; however, its application in mental health still requires a consolidated understanding of existing findings regarding effectiveness. This bibliometric study aims to synthesize current knowledge and explore research trends related to AI's role in mental health. It investigates how advancements in modern technologies are used to predict, prevent, and treat mental disorders, and evaluates their effectiveness. A literature search was conducted using Lens software to retrieve peer-reviewed empirical studies in English from highly ranked databases, covering the period from 2005 to 2025. A total of 97 relevant publications were identified and analyzed for patterns, trends, and associations using the Bibliometrix package in R. Results reveal a sharp increase in publications after 2020. Clinical and applied psychology emerged as dominant fields. Eating and Weight Disorders is the leading journal (n=22), followed by the Journal of Psychopathology and Behavioral Assessment (n=19) and Cognitive Therapy and Research (n=17). The United States is both the most productive (n=149) and most cited country (n=8,896). AI has demonstrated promise in detecting symptoms of depression and suicidal behavior, preventing mental health disorders, and enhancing traditional psychological interventions. Nonetheless, several gaps remain, including the underrepresentation of diverse populations and a limited understanding of factors influencing user acceptance of AI-based tools. This study provides researchers with an overview of publication trends, collaboration networks, keyword analysis, and future research directions. It also supports practitioners in selecting appropriate AI-based interventions to improve mental health outcomes and overall well-being within healthcare systems.