ArticleDigital health
Generative artificial intelligence in depression research: A bibliometric analysis of WoSCC-Indexed literature.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.