CMR Surgical, Nvidia showcase predictive surgical robotic capabilities – MassDevice

CMR Surgical and Nvidia are showcasing predictive surgical robotic capabilities at the 2026 Society of Robotic Surgery (SRS) meeting, according to a report by MassDevice on July 23, 2026. The Cambridge, UK-based CMR Surgical is demonstrating a system it describes as a new paradigm of simulation in surgical robotics to improve how surgeons prepare for and execute procedures.

The collaboration focuses on integrating Nvidia’s computing power and simulation tools with CMR Surgical’s robotic platforms. This integration aims to move beyond static training models toward predictive simulations that can better anticipate surgical outcomes and instrument interactions.

Nvidia Simulation Integration in Surgical Robotics

The technology being demonstrated at SRS 2026 leverages Nvidia’s AI and physics-based simulation capabilities. According to MassDevice, the goal is to create high-fidelity environments where surgeons can practice complex maneuvers with predictive feedback, reducing the gap between simulated training and live operating room performance.

Predictive capabilities in this context refer to the system’s ability to simulate how tissues and organs will react to robotic instruments in real-time. By using Nvidia’s hardware and software stacks, CMR Surgical aims to provide a more accurate representation of surgical anatomy and instrument dynamics than previous simulation generations offered.

Strategic Impact on Surgical Training

The shift toward predictive simulation addresses a long-standing challenge in robotic surgery: the transition from synthetic trainers to human patients. By implementing what CMR Surgical calls a new paradigm, the company intends to standardize the proficiency levels of surgeons before they enter the operating theater.

This approach relies on the intersection of three specific technical layers: the robotic hardware of CMR Surgical, the simulation engines provided by Nvidia, and the predictive AI models that analyze surgical data to forecast potential complications or optimal paths of movement during a procedure.

The demonstration at SRS 2026 serves as a proof-of-concept for how AI-driven simulation can be scaled across different surgical specialties. The predictive nature of the software allows for the creation of patient-specific simulations, where a surgeon can practice on a digital twin of a specific patient’s anatomy before the actual surgery begins.