ArticleDigital health
Mapping scientific trends in bipolar disorder and digital psychiatry (2000-2025): A bibliometric and visualized analysis of AI-driven diagnosis and digital interventions.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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.
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Who cites it
2 citing papers in PubMed.
- Hybrid Explainable Deep Ensemble Framework with self-attention mechanism for early prediction of depression severity using PHQ-9 clinical and behavioral data.Annals of medicine and surgery (2012) · 2026Article
- Advancing the understanding of tumor microenvironment through medical imaging based on bibliometric analysis.Translational cancer research · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: This study aims to map and visualize global research trends related to bipolar disorder (BD), with a focus on artificial intelligence (AI)-driven diagnosis and digital psychiatry from 2000 to 2025. Methods: A bibliometric analysis was conducted using publications retrieved from Web of Science, Scopus, and PubMed, covering the period from January 1, 2000, to June 30, 2025. All data were extracted from databases at the time of search in mid-2025, and no future or projected data were included. After deduplication and data cleaning, 4,753 relevant articles were included. VOSviewer was utilized to perform co-authorship, keyword co-occurrence, and bibliographic coupling network analysis. Descriptive statistics, including annual publication trends, authorship patterns, and institutional contributions, were analyzed using Excel Pivot Tables. Results: Scientific output in this field has grown significantly since 2015, with the United States (n=1850), United Kingdom (n=750), and China (n=620) leading in publications. Harvard University emerged as the most prolific institution. Author collaboration networks identified Eduard Vieta and Lars Vedel Kessing as key contributors. Keyword analysis revealed four dominant clusters: AI/machine learning, clinical diagnosis and biomarkers, cognitive function and quality of life, and digital interventions via smartphones and wearables. Journals such as Conclusion: Research in BD is shifting toward AI-enabled and digital mental health approaches. Future studies should enhance interdisciplinary collaboration, focus on multimodal data integration, and promote equitable global access to digital psychiatric tools.
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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.