Trends and research clusters in artificial intelligence-assisted ultrasound-guided regional anesthesia: A bibliometric analysis.

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). The British Journal of Anaesthesia had the highest publication count (TP = 7) and h-index (h = 6), whereas Regional Anesthesia and Pain Medicine had the highest total citation count (TC = 225). Bowness JS stood out both in terms of productivity and citation influence. Keyword co-occurrence analysis identified eight algorithmically derived clusters, which were subsequently consolidated into three dominant thematic research hotspots: accurate identification of anatomical structures, risk classification and prediction of perioperative complications, and guidelines and education in UGRA. Temporal keyword burst detection revealed "nerve block", "brachial plexus", and "American Society" as newly intensified focal points within the field. 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.