ArticleDigital health
Trends and research clusters in artificial intelligence-assisted ultrasound-guided regional anesthesia: 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. Not yet cited in PubMed.
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
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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: This exploratory bibliometric study aimed to characterize publication trends, research clusters, and emerging topics in artificial intelligence (AI)-assisted ultrasound-guided regional anesthesia (UGRA). Methods: We searched the Web of Science Core Collection for English-language articles published or indexed from January 1, 2008, through December 10, 2025. Two independent reviewers performed eligibility assessment; discrepancies were reconciled via joint deliberation. Quantitative bibliometric processing was conducted using VOSviewer, CiteSpace, and the R package "bibliometrix". Results: Ninety publications from 33 countries or territories and 223 institutions were included. Annual output increased overall but fluctuated, peaking in 2021 with 17 publications. China contributed the highest volume (28 works), trailed by the United States (14) and the United Kingdom (12). The University of London ranked first in output volume (18 contributions). Conclusion: This exploratory bibliometric analysis summarizes the principal research themes in AI-assisted UGRA. Future studies should evaluate technical performance together with clinician-rated usability and patient-relevant outcomes and should validate AI tools across institutions, devices, and patient populations.
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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.