The emerging landscape of artificial intelligence in robot-assisted surgery: a bibliometric and visualization analysis

This study aims to systematically examine the research landscape, developmental trajectories, and emerging hotspots of artificial intelligence (AI) in robot-assisted surgery through bibliometric analysis. It further seeks to identify critical barriers along the pathway from technological validation to clinical translation and to provide visualized evidence for informing strategic planning in future research. A total of 1176 peer-reviewed articles published between 2014 and 2025 were retrieved from the Web of Science Core Collection database. Using integrated bibliometric tools—including Bibliometrix (R), VOSviewer, and CiteSpace—we conducted a comprehensive analysis of publication trends, international collaboration networks, keyword clustering, and citation bursts to map the intellectual and conceptual structure of the field. Research output exhibited an average annual growth rate of 35.56%, peaking in 2024. While the United States, China, and the United Kingdom ranked highest in publication volume, normalized impact metrics such as H-Index revealed stronger relative influence among several European countries. Thematic evolution shifted from early-phase focuses on computer vision and instrument tracking (2014–2018) toward recent priorities including surgical skill assessment, intraoperative decision support, and ethical governance, with “ethical considerations” emerging as a salient keyword. Despite growing scholarly interest, there remains a marked deficiency in high-quality prospective multicenter randomized controlled trials and rigorous cost-effectiveness evaluations—key gaps that hinder robust clinical translation. Future efforts should prioritize the establishment of international data-sharing platforms, advancement of methodologically sound clinical trials, development of domain-specific ethical frameworks, and innovation in affordable AI-integrated surgical solutions. This study presents a descriptive bibliometric analysis that maps the current state and evolutionary trajectory of AI applications in robot-assisted surgery. While AI currently functions primarily in an assistive capacity, its clinical integration faces multifaceted challenges related to evidentiary support, data accessibility, ethical oversight, and economic feasibility. Advancing AI toward safe, effective, and equitable clinical deployment will require sustained interdisciplinary collaboration and the implementation of systematic evaluation frameworks.