Optimization of Regional Anesthesia Through Robotic Needle Guidance

Abstract

Robotic needle guidance has moved from a single technical demonstration a decade ago into a broader, still-emerging subfield of regional anesthesia that combines robotic actuation, force feedback, and artificial-intelligence-based image interpretation. This review traces that development from the first robot-assisted human nerve blocks performed with the Magellan system through simulation studies comparing robotic and manual needle guidance, the mechanical solutions borrowed from adjacent epidural and lumbar puncture robotics, and the deep-learning segmentation models now being used to identify nerve and vascular targets on ultrasound in real time. Simulation-based evidence indicates that robotic assistance narrows the performance gap between novice and experienced operators more than it accelerates task completion for already-skilled hands, while force-sensing and puncture-detection systems developed for neuraxial robotics offer a plausible route toward mechanical safeguards against intraneural or intravascular needle placement. Artificial-intelligence-based nerve segmentation has matured technically but is hampered by a mismatch between standard computer-vision performance metrics and the clinically relevant question of whether a given segmentation would keep a needle tip out of harm's way, and no adequately powered randomized trial yet compares robot-assisted to conventional ultrasound-guided nerve block on patient-centered outcomes. We conclude that robotic needle guidance is a credible and rapidly maturing research direction, with the greatest promise lying in integrated systems that combine reliable AI-based target identification with robotic path execution, rather than in either technology deployed alone.