Collaborative Computing in Interventional Medicine

With the rapidly growing popularity of the Intuitive Surgical da Vinci system, robotic minimally invasive surgery (RMIS) has crossed the threshold from the laboratory to the real world. There are now hundreds of thousands of human-guided robotic surgeries performed every year, and the number is growing rapidly. This is a unique example of the larger paradigm of human-machine collaborative systems – namely systems of robots collaborating with people to perform tasks that neither can perform as effectively alone.

In this talk, I will describe our work toward developing effective human-machine collaborative teams.  Our research largely focuses on two inter-related problems: 1) recognizing and responding to the intent of the user and 2) evaluating the effectiveness of their performance. We consider surgery to be composed of a set of identifiable tasks which themselves are composed of a small set of reusable motion units that we call "surgemes." By creating models of this "Language of Surgery," we are able to evaluate the style and efficiency of surgical motion. These models also lead naturally to methods for effective training of RMIS using automatically learned models of expertise, and toward methods for supporting or even automating component actions in surgery. I will close by briefly describing our aspirations for this work through a recently funded National Robotics Initiative consortium.