A Cyber-Physical Readiness Framework for Robot-Assisted Surgery: Scenario-Based Assessment of XR and Teleoperation

Abstract Robot-assisted surgery requires more than an operative console: clinical deployment depends on instruments, visualization and extended reality (XR), network reliability, team training, credentialing, and governance. This study develops a five-layer cyber-physical readiness framework for comparing elective robotassisted surgery, XR-focused robotic training, and networked teleoperation. A weighted multi-criteria index combines literature-informed layer scores for console control, instruments, vision/XR, network capability, and governance. The model includes scenario-dependent weights, an illustrative model threshold, and a teleoperation safety gate requiring acceptable latency, authentication, fail-safe stopping, local recovery capability, and clear credentialing and liability arrangements. Under the coded illustrative inputs, elective robot-assisted surgery meets the model threshold (R = 0.736), XR-focused training lies near it (R = 0.674), and remote teleoperation remains below it (R = 0.619) and fails the safety gate. Sensitivity and uncertainty analyses identify network assurance, fail-safe coverage, console performance, and governance as high-leverage factors. The framework is a transparent program-level decision aid; it is not a validated clinical prediction model, regulatory score, or substitute for procedure- and site-specific safety assessment.