Machine Learning and Medical Education: Impending Conflicts in Robotic Surgery

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?

The technology

This is not just sci-fi. Given the direction robotic surgery is heading, it is increasingly likely to become reality.

The Da Vinci system has become a regular feature in the operating theater, optimizing many laparoscopic procedures in gynecology, urology, and general surgery. Although it has the potential for remote control and automation its present clinical use is limited to operation by a human surgeon in the same room. Increasingly, other robots are improving performance and outcomes by contributing to planning and decision-making as well as technical skills. The Soft Tissue Autonomous Robot (STAR) has shown that automated robots can form more reliable connections between sections of bowel than human surgeons. We must anticipate a watershed moment when robots are able to plan and perform entire operations without the input of human surgeons.

Machine learning is one precondition for such robot-led operations. Machine learning is the process whereby computers optimize their algorithms through feedback, allowing machines to perform tasks without prior programming. The underlying concepts behind deep-learning neural networks have been around for many years, but have only recently come to the fore due to increasing computational power. Recent applications to healthcare have included the diagnosis of melanoma and the DREAM system for diagnosing diabetic retinopathy.

The UC Berkeley Centre for Automation and Learning for Medical Robotics (CAL-MR)is now integrating machine learning and the Da Vinci system. Given the complexity and delicacy of human soft tissue, these researchers believe that programming a robot to operate on internal organs, the model used by STAR, would be improved by allowing robots to learn for themselves.

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