Artificial Intelligence and Mental Health: Research Trends, Hotspots, and Emerging Themes (2004–2024)

Background and Aims: Mental health disorders constitute a major global health burden, while mental health services remain inadequately resourced, particularly in low- and middle-income countries. Recent advances in Artificial Intelligence (AI), including machine learning, deep learning, and natural language processing, have opened new possibilities for the early detection, diagnosis, monitoring, and treatment of mental health conditions. Despite rapid growth, research in this field is highly interdisciplinary and fragmented, limiting understanding of its overall development. Methods: A retrospective cross-sectional bibliometric analysis was conducted using the Scopus database. Peer-reviewed journal articles published between January 1, 2004, and December 31, 2024, were retrieved through title-based searches combining AI- and mental health-related terms. Bibliometric indicators, including publication output, authors, institutions, countries, journals, funding agencies, and highly cited articles, were analyzed. Science-mapping techniques and network visualization using VOS viewer were employed to explore intellectual, social, and conceptual research structures. Results: A total of 14,904 publications were included. Research growth was modest until 2015 but accelerated markedly after 2019 (R² = 0.9685). The field was dominated by medicine, neuroscience, and computer science. The United States, China, and the United Kingdom emerged as the leading contributors in both productivity and international collaboration. Key research hotspots included AI-driven neuroimaging, natural language processing for mental health assessment, social media–based surveillance, and digital mental health interventions. Conclusion: AI research in mental health has expanded rapidly, progressing from neurobiological modeling to scalable digital applications. Future advancement will require ethical governance, representative datasets, interdisciplinary collaboration, and robust clinical validation to ensure safe, effective, and equitable implementation.