From Clinical Technique to Interprofessional Implementation and Intelligent Integration: A Bibliometric Analysis of the Interdisciplinary Expansion of Robot‐Assisted Spine Surgery - Xiaoping Wang; Yingdi Yuan; Qifei Duan; Youhui Wu (The International Journal of Medical Robotics and Computer Assisted Surgery, 2026)
ABSTRACT Background Robot‐assisted spine surgery (RASS) has expanded beyond technical accuracy towards interprofessional, perioperative, and AI‐driven implementation, yet this interdisciplinary evolution has not been systematically characterised. Methods We analysed 327 Web of Science Core Collection publications (2006–2026) at the intersection of RASS and perioperative, collaborative, or educational research. Analyses included co‐authorship networks, keyword co‐occurrence, co‐citation mapping, bibliographic coupling, citation‐burst detection, Callon strategic mapping, and dual‐map overlays, performed in VOSviewer, CiteSpace, and Bibliometrix. Ethical approval was not required as only de‐identified bibliographic records were analysed. Results Publications grew at a compound annual rate of 17.28% over the complete years 2006–2025 (log‐linear model of annual output, R 2 = 0.8762), peaking at 62 publications in 2025; the partial year 2026 (indexed only to the 4 February 2026 search date) was excluded from all growth‐rate and trendline modelling. The United States ( n = 137) and China ( n = 57) dominated output. Dual‐map analysis indicated citation flows into the (broad) Health/Nursing/Medicine domain. Keyword bursts identified artificial intelligence (strength = 3.13), deep learning (2.01), and augmented reality (1.75) as the most recent (2023–2025) research frontiers. Rehabilitation robotics remained bibliographically isolated from the surgical core. Conclusions The published literature on RASS shows a measurable thematic shift from procedural‐accuracy validation towards perioperative‐implementation and intelligent‐integration topics; whether this bibliometric shift reflects a corresponding change in clinical practice cannot be determined from publication data. These patterns nonetheless motivate investment in cross‐disciplinary consortia, AI‐competency frameworks, and attention to implementation equity.