Artificial intelligence and digital health in vascular surgery: a 2-decade bibliometric analysis of research landscapes and evolving frontiers.

To analyze the structural and temporal evolution of artificial intelligence (AI) and digital health applications in vascular surgery over the past two decades, identifying historical development trajectories, research focal points, and emerging frontiers. Publications on AI and digital health applications in vascular surgery were retrieved from WoSCC. Analyzed through CiteSpace and HistCite to track temporal development, thematic shifts, and innovation patterns within the domain. Active themes have emerged over time, with 123 related disciplines, 505 keywords, and 675 outbreak papers cited. Keyword clustering anchors seven emerging research subfields, namely #0 deep learning, #2 machine learning, #3 peripheral arterial disease, #4 renal cell carcinoma, #5 aortic aneurysm, #6 pulmonary embolism, #7nanocarrier. The alluvial map indicates that the most enduring research concepts within the domain include bypass, revascularisation, and others, while emerging keywords consist of chronic limb-threatening ischemia and peripheral vascular intervention, among others. Reference clustering identifies seven recent subfields of research: nephrectomy #0, force #1, artificial intelligence #2, navigation #4, prediction #5, augmented reality #9, and telemedicine #13. This study provides a comprehensive mapping of AI and digital health adoption in vascular surgery, delineating paradigm shifts from traditional surgical techniques to computational prediction models and intelligent intervention systems. The findings establish foundational references for prioritizing research investments and developing standardized evaluation metrics for emerging technologies.