Generative artificial intelligence in depression research: A bibliometric analysis of WoSCC-Indexed literature

Abstract

Background: Depression is a leading global cause of disability. The rapid emergence of generative artificial intelligence (GenAI), particularly large language models (LLMs) like ChatGPT, offers new opportunities for digital psychiatry. However, the Web of Science Core Collection (WoSCC) -indexed research landscape of GenAI in depression has not yet been systematically mapped. Objective: This study aimed to systematically evaluate the WoSCC-indexed research landscape, hotspots, and emerging trends of GenAI in depression through bibliometric analysis. Methods: A bibliometric analysis was conducted on publications from the WoSCC (January 2023–July 2025). Additionally, PubMed was searched to identify relevant clinical and translational studies for contextual interpretation. Analyses utilized CiteSpace, VOSviewer, and Bibliometrix. Results: We identified 115 publications, with publication output increasing markedly from 2023 to mid-2025. The United States and China led in volume, with Harvard University as a key contributor. International collaboration involved 39 countries but remained regionally concentrated. Co-citation analysis revealed 10 clusters, including depression management, deep learning, and NLP. Keyword bursts highlighted trends in “large language models,” “ChatGPT,” and “digital health.” Top-cited works focused on conversational agents and LLM evaluation. Conclusion: This study provides one of the first bibliometric analyses of WoSCC-indexed research on GenAI in depression, highlighting increasing scholarly attention to conversational agents, large language models, and digital mental health applications. Future research should prioritize clinical validation, safety, and interdisciplinary collaboration to strengthen the evidence base for responsible implementation.