ArticleDigital health
Global research landscape and emerging trends in the application of artificial intelligence in depression: A bibliometric analysis.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
1 citing paper in PubMed.
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Depression has emerged as a significant public health concern in modern society, while conventional medical approaches exhibit notable limitations in its diagnosis and treatment. With the rapid advancement of technologies such as large-scale artificial intelligence (AI) models, the application of AI to drive research and innovation in mental health-and to further its translational implementation in clinical practice-has garnered increasing attention. Objective: Focusing on the intersection of AI and depression, this bibliometric study aims to reveal key themes and developmental trajectories within this domain. Methods: A bibliometric analysis was performed on publications retrieved from the Web of Science Core Collection. Utilizing the Bibliometrix package, CiteSpace, and VOSviewer, we quantified publication output, collaboration networks, and key research themes. The analysis encompassed English-language articles published between 2011 and 2024. Results: A total of 1361 publications met the inclusion criteria. Since 2011, the annual number of publications on AI in depression has demonstrated a steady upward trajectory, entering a phase of accelerated growth after 2020. The peak annual output exceeded 300 articles. Analysis of geographical and institutional contributions identified China and the United States as the leading countries, with Lanzhou University being the most productive institution. The most prolific authors were Bin Hu, Xiaowei Li, and Raymond W. Lam, while R.C. Kessler emerged as the most cocited author. Keywords such as "machine learning," "deep learning," and "feature extraction" showed a marked increase in frequency, reflecting the field's evolving technical focus. Furthermore, the development of international collaborations underscores the increasingly globalized nature of research in this domain. Conclusion: This study presents the latest comprehensive bibliometric analysis results of AI in the field of depression, clarifying current research hotspots and directions, and providing important resources for clinicians and researchers.
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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.