Robot-assisted spinal surgery: technological maturity, clinical evidence, and future directions.

Abstract

Robot-assisted spinal surgery has evolved from bone-mounted trajectory guidance to integrated navigation and, in selected research systems, constrained task execution. This evolution has been accompanied by rapid commercial and academic activity, but regulatory status, technical capability, and clinical effectiveness are often conflated. Such conflation can overstate both the maturity of emerging platforms and the certainty of their patient-level benefits. This structured narrative review was developed from a reproducible PubMed search, targeted citation chasing, Crossref metadata verification, and model-specific regulatory checks. Evidence was organized by source class and by outcome domain rather than by manufacturer chronology alone. Established platforms were separated from early clinical and preclinical systems, and regulatory clearance or registration was treated as distinct from evidence of comparative effectiveness. The most consistent evidence concerns pedicle-screw placement accuracy. Comparative studies and evidence syntheses generally favor robot-assisted placement over freehand or fluoroscopy-guided techniques, although the magnitude and consistency of benefit vary by platform, comparator, grading system, and surgeon experience. Radiation exposure may be reduced in some workflows, but acquisition protocols and reporting units are heterogeneous. Evidence for operative time, complications, revision surgery, patient-reported outcomes, and cost-effectiveness remains inconsistent or sparse. The principal contribution of this review is an evidence-maturity framework that links each technical capability to the strongest available clinical evidence and explicitly separates premarket regulatory decisions from claims of clinical superiority. Robot-assisted systems should currently be understood as surgeon-controlled enabling technologies. Future progress will depend less on adding degrees of freedom than on demonstrating reproducible patient benefit, safe task autonomy, interoperable data pipelines, and value across health-care settings.