ArticleInternational journal of environmental research and public health2019
The Current Research Landscape on the Artificial Intelligence Application in the Management of Depressive Disorders: A Bibliometric Analysis.
Article in International journal of environmental research and public health, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 4 of them syntheses that pooled it.
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.
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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
35 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Artificial Intelligence in Malnutrition: A Systematic Literature Review.Advances in nutrition (Bethesda, Md.) · 2024Pooled it
- Meta-analysis of 5-hydroxytryptamine transporter gene promoter region polymorphism and post-stroke depression.The Journal of international medical research · 2020Pooled it
- Metastatic castration-resistant prostate cancer: Academic insights and perspectives through bibliometric analysis.Medicine · 2020Pooled it
- What Does Personality Mean in the Context of Mental Health? A Topic Modeling Approach Based on Abstracts Published in Pubmed Over the Last 5 Years.Frontiers in psychiatry · 2019Pooled it
- Article
- The ethics of artificial intelligence systems in healthcare and medicine: from a local to a global perspective, and back.Pflugers Archiv : European journal of physiology · 2025Review
- Global output of clinical application research on artificial intelligence in the past decade: a scientometric study and science mapping.Systematic reviews · 2025Article
- Global trends of purinergic receptors and depression: A bibliometric analysis from 2003 to 2023.World journal of psychiatry · 2025Article
- Article
- From Serendipity to Precision: Integrating AI, Multi-Omics, and Human-Specific Models for Personalized Neuropsychiatric Care.Biomedicines · 2025Review
- Resting-State Electroencephalogram Depression Diagnosis Based on Traditional Machine Learning and Deep Learning: A Comparative Analysis.Sensors (Basel, Switzerland) · 2024Review
- Debate and Dilemmas Regarding Generative AI in Mental Health Care: Scoping Review.Interactive journal of medical research · 2024Article
- The 100 most cited articles in artificial intelligence related to orthopedics.Frontiers in surgery · 2024Article
- Examining Analytic Practices in Latent Dirichlet Allocation Within Psychological Science: Scoping Review.Journal of medical Internet research · 2022Article
- Plant-Based Meat Analogues from Alternative Protein: A Systematic Literature Review.Foods (Basel, Switzerland) · 2022Review
- Using a Virtual Patient via an Artificial Intelligence Chatbot to Develop Dental Students' Diagnostic Skills.International journal of environmental research and public health · 2022Article
- Burnout amongst radiologists: A bibliometric study from 1993 to 2020.World journal of psychiatry · 2022Article
- Changes of adrenocorticotropic hormone rhythm and cortisol circadian rhythm in patients with depression complicated with anxiety and their effects on the psychological state of patients.Frontiers in psychiatry · 2022Article
- Review
- Machine Learning for Mental Health in Social Media: Bibliometric Study.Journal of medical Internet research · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI)-based techniques have been widely applied in depression research and treatment. Nonetheless, there is currently no systematic review or bibliometric analysis in the medical literature about the applications of AI in depression. We performed a bibliometric analysis of the current research landscape, which objectively evaluates the productivity of global researchers or institutions in this field, along with exploratory factor analysis (EFA) and latent dirichlet allocation (LDA). From 2010 onwards, the total number of papers and citations on using AI to manage depressive disorder have risen considerably. In terms of global AI research network, researchers from the United States were the major contributors to this field. Exploratory factor analysis showed that the most well-studied application of AI was the utilization of machine learning to identify clinical characteristics in depression, which accounted for more than 60% of all publications. Latent dirichlet allocation identified specific research themes, which include diagnosis accuracy, structural imaging techniques, gene testing, drug development, pattern recognition, and electroencephalography (EEG)-based diagnosis. Although the rapid development and widespread use of AI provide various benefits for both health providers and patients, interventions to enhance privacy and confidentiality issues are still limited and require further research.
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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.