Human-generated, preventable errors, particularly those made intra-operatively, can lead to morbidity and mortality for the patient, poor training outcomes for residents, and high costs for the hospital. Surgical robotic systems could be designed to avoid these errors and improve training outcomes by interpreting, reacting to, and assisting human behavior. This talk will describe some novel data-driven methods to predict, in real-time, surgical style, expertise levels, and task difficulty; as well as present new systems that could be used to assist with surgical intervention or training in a variety of domains.
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