ArticleThe international journal of medical robotics + computer assisted surgery : MRCAS2026
From Clinical Technique to Interprofessional Implementation and Intelligent Integration: A Bibliometric Analysis of the Interdisciplinary Expansion of Robot-Assisted Spine Surgery.
Article in The international journal of medical robotics + computer assisted surgery : MRCAS, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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Authors and funding
4 authors.
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Abstract
backgroundRobot-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.
methodsWe 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.
resultsPublications grew at a compound annual rate of 17.28% over the complete years 2006-2025 (log-linear model of annual output, R
conclusionsThe 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.