Autonomous surgical robot makes strides in soft-tissue precision

Editor's Note

Capability to achieve results on par with or better than humans using laparoscopic techniques demonstrates the extent to which autonomous surgical robots are rapidly evolving toward clinical readiness, according to John Hopkins University robotics researchers writing may 27 in IEEE Spectrum.

The system detailed in the article, Johns Hopkins’ Smart Tissue Autonomous Robot (STAR), has progressed from early open-surgery trials to fully autonomous laparoscopic soft-tissue operations in animal models. In the latest generation, STAR autonomously placed nearly six stitches before requiring human correction—significantly outperforming manual laparoscopic surgery, where surgeons typically adjust the needle after nearly every stitch, the researchers write. STAR achieves this through a combination of AI-guided surgical planning, real-time 3D imaging, and improved mechanical dexterity.

As detailed in the article, key technical advances include:

• A dual-camera system that combines high-resolution color imaging with near-infrared tracking to monitor tissue deformation in real time.
• Miniaturized endoscopes capable of capturing depth and motion data during laparoscopic procedures.
• Machine learning algorithms that compensate for respiration-related tissue movement and eliminate the need for visual tracking markers.
• Enhanced robotic arms and tools that mimic wrist movements for greater control in tight surgical spaces.

Authors also claim STAR’s ability to generate and adapt surgical plans on the fly marks a shift from supervised autonomy to task autonomy—allowing it to perform most steps independently under human oversight. This is a foundational step toward future systems that may eventually manage entire procedures and, as detailed in the article, fill gaps in global surgical workforces.

However, authors also underscore major barriers: unlike factory robots, surgical robots must navigate unpredictable, deformable anatomy, requiring AI systems that can adapt in real time. Ethical and legal concerns—especially regarding liability in autonomous decision-making—remain unresolved. Future systems will need to incorporate speech recognition, natural language understanding, and generalizable robotic controllers trained on vast surgical datasets.

Accompanied by a wealth of images, the full report offers additional detail and context on the technology, the evolving landscape of robotic surgery, and the potential promise of automation in surgical services.