Robot-assisted stereoelectroencephalography for drug-resistant epilepsy: Workflow optimization, implementation challenges, and future direction

Robot-assisted stereoelectroencephalography (RA-SEEG) has become an increasingly important implantation strategy for patients with drug-resistant epilepsy who require invasive exploration of three-dimensional epileptic networks. This narrative review synthesizes current evidence on RA-SEEG as a workflow-optimizing intervention rather than a stand-alone device substitute. A targeted literature update was performed before submission. Priority was given to systematic reviews, meta-analyses, recent comparative studies, pediatric series, platform-specific reports, computer-assisted planning studies, and contemporary reviews of SEEG methodology published through May 2026. No new meta-analysis was conducted. Evidence was grouped into five domains: implantation precision, safety, procedural efficiency, clinical translation, and platform/automation heterogeneity. The available literature supports the feasibility, safety, and high technical accuracy of RA-SEEG, particularly when integrated with rigorous multimodal imaging, vascular avoidance, expert trajectory planning, registration quality control, and postoperative verification. Comparative and meta-analytic evidence suggests that RA-SEEG can reduce overall operative time and time per electrode while maintaining broadly comparable safety and accuracy to optimized frame-based approaches. However, claims of universal superiority remain limited by heterogeneous accuracy metrics, robotic platforms, registration methods, comparator workflows, and outcome definitions. The strongest current value proposition is reproducibility and workflow efficiency, not automatic improvement in seizure freedom. Future research should move beyond isolated technical endpoints toward prospective multicenter studies linking implantation metrics to diagnostic yield, treatment selection, complications, neuropsychological outcomes, quality of life, and cost-effectiveness. Human-in-the-loop artificial intelligence and computer-assisted planning may further reshape RA-SEEG, but their clinical use requires transparent validation, accountability, and safeguards against automation bias.