Work

From Clinical Technique to Interprofessional Implementation and Intelligent Integration: A Bibliometric Analysis of the Interdisciplinary Expansion of Robot‐Assisted Spine Surgery

Year: 2026

Type: article

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: = 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.

Source: International Journal of Medical Robotics and Computer Assisted Surgery

DOI: 10.1002/rcs.70229

Authors: Xiaoping Wang, Yingdi Yuan, Qifei Duan, Youhui Wu

Institution: Shantou University Medical College

Language: English

Topic: scientometrics and bibliometrics research

Subfield: Statistics, Probability and Uncertainty

Field: Decision Sciences

Domain: Social Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $4,430