robotics

Cross Post: Machine Learning and Medical Education: Impending Conflicts in Robotic Surgery

Guest Post by Nathan Hodson 

* Please note that this article is being cross-posted from the Journal of Medical Ethics Blog 

Research in robotics promises to revolutionize surgery. The Da Vinci system has already brought the first fruits of the revolution into the operating theater through remote controlled laparoscopic (or “keyhole”) surgery. New developments are going further, augmenting the human surgeon and moving toward a future with fully autonomous robotic surgeons. Through machine learning, these robotic surgeons will likely one day supersede their makers and ultimately squeeze human surgical trainees out of operating room.

This possibility raises new questions for those building and programming healthcare robots. In their recent essay entitled “Robot Autonomy for Surgery,” Michael Yip and Nikhil Das echoed a common assumption in health robotics research: “human surgeons [will] still play a large role in ensuring the safety of the patient.” If human surgical training is impaired by robotic surgery, however—as I argue it likely will be—then this safety net would not necessarily hold.

Imagine an operating theater. The autonomous robot surgeon makes an unorthodox move. The human surgeon observer is alarmed. As the surgeon reaches to take control, the robot issues an instruction: “Step away. Based on data from every single operation performed this year, by all automated robots around the world, the approach I am taking is the best.”

Should we trust the robot? Should we doubt the human expert? Shouldn’t we play it safe—but what would that mean in this scenario? Could such a future really materialize?

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