Researchers in the United States say their experimental AI-guided robot can autonomously perform a delicate, complicated phase of a common gallbladder operation, marking a major step toward automated medical procedures, reports Reuters.
Existing surgical robots are remotely controlled by surgeons, but the new system uses artificial intelligence to make independent decisions and adapts to unexpected complications during operations, said Axel Krieger of Johns Hopkins University in Baltimore, who led the research.
He likened it to an autonomous vehicle that can “navigate any road, in any condition, responding intelligently to whatever it encounters”.
“This advancement moves us from robots that can execute specific surgical tasks to ones that truly understand surgical procedures,” he said.
The SRT-H robot was trained via an AI framework known as language-guided imitation learning, using videos of surgeons performing gallbladder removal surgeries on pig cadavers, the researchers reported in Science Robotics.
The robot was tested on eight varying sets of pig gallbladders and livers that had been removed from the animals.
Separating the gallbladder from the liver takes several minutes and involves “diverse tool use, including grabbing, clipping and cutting – skills common in real surgical procedures”, along with decision-making and adaptation, the researchers said.
The pig organs and blood vessels in the tests varied widely in appearance and anatomy, “mirroring the diversity encountered in human surgeries”, they added.
While the robot achieved 100% accuracy in the surgeries, it took longer to perform the work than a human surgeon.
Commercially available surgical robots include Intuitive Surgical’s da Vinci Surgical System, which has been used in more than 12m procedures globally since receiving FDA approval in 2000.
Unlike SRT-H, the Da Vinci system relies entirely on human surgeons to control its movements remotely.
The global surgical robotics market is approaching $10bn annually, with about 2.7m robotic procedures performed in 2024.
Eventually, autonomous surgical robots could help tackle surgeon shortages, minimise human error, and provide consistent, high-quality care in underserved regions, the researchers said.
Study details
SRT-H: A hierarchical framework for autonomous surgery via language-conditioned imitation learning
Ji Woong (Brian) Kim, Juo-Tung Chen, Pascal Hansen, Axel Krieger et al.
Published in Science Robotics on 9 July 2025
Abstract
Research on autonomous surgery has largely focused on simple task automation in controlled environments. However, real-world surgical applications demand dexterous manipulation over extended durations and robust generalisation to the inherent variability of human tissue. These challenges remain difficult to address using existing logic-based or conventional end-to-end learning strategies. To address this gap, we propose a hierarchical framework for performing dexterous, long-horizon surgical steps. Our approach uses a high-level policy for task planning and a low-level policy for generating low-level trajectories. The high-level planner plans in language space, generating task-level or corrective instructions that guide the robot through the long-horizon steps and help recover from errors made by the low-level policy. We validated our framework through ex vivo experiments on cholecystectomy, a commonly practiced minimally invasive procedure, and conducted ablation studies to evaluate key components of the system. Our method achieves a 100% success rate across eight different ex vivo gallbladders, operating fully autonomously without human intervention. The hierarchical approach improved the policy’s ability to recover from suboptimal states that are inevitable in the highly dynamic environment of realistic surgical applications. This work demonstrates step-level autonomy in a surgical procedure, marking a milestone toward clinical deployment of autonomous surgical systems.
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