Robotic Surgery Just Got More Autonomous
Soon it may be possible to receive surgery from a robot. It will just be soft tissue surgery and nothing too complicated, but it will be a step in the right direction.

Soon it may be possible to receive surgery from a robot. It will just be soft tissue surgery and nothing too complicated, but it will be a step in the right direction. Research so far has been limited to animals, but the robot performed to a higher level than some top surgeons and other techniques involving robots when carrying out open bowel operations on pigs.
There is an estimated 45 million soft tissue surgical procedures carried out in the United States each year, and it is hoped that the new robot will cut back on complications, and be more efficient than the present system. The present system consists of surgeons working the robot, and their ability is paramount.
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The process is further advanced when it comes to hard tissue operations, but as soft tissues are malleable it is harder to work on. Named STAR – Smart Tissue Autonomous Robot – was designed by Azad Shademan and is made up of a robotic arm and surgical equipment. It operates by using fluorescent markers and smart imaging technologies allowing it to cope with the issues faced when operating on moving tissue.
While being supervised, STAR came out on top when it came to procedures such as stitching and rejoining sections of bowel. The pigs survived the surgery, and there were no complications.
With further research, it is expected that the use of robots will make surgery safer for humans and mean that there will no longer be human error in the operating arena. As well as bowel surgery, it robot will remove tumors.
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Daniel Okafor
Daniel Okafor is the Senior AI Reporter at TrendinTech, where he covers large language models, machine learning research and the practical use of artificial intelligence across business and government. He previously reported on artificial intelligence for MIT Technology Review, covering the labs behind the current generation of frontier models and the policy debates in Washington and Brussels. Daniel holds a Master of Science in Machine Learning from Carnegie Mellon University and follows the research community closely, attending NeurIPS and ICML each year to speak with the people behind the papers. He has a particular interest in evaluation: how models are benchmarked, where those benchmarks fail and what that means for the companies betting on them.
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