The clinical version of the (DVRK) is becoming an important tool of application in minimally invasive surgery as laparoscopic robotic operations. Previous investigations performed with the research version of the robot (Not used in humans) have caused impact on the clinical version (Used in humans). Using this last one can minimize patient recovery time, perform faster procedures, and reduce fatigue in surgeons. This study aims to explore how Robotic Assistive Surgery (RAS) can improve patient outcomes and to discuss when a mistake during a procedure is caused by human error or instruments failures. Investing in this technology could improve not only the features of the robot, but also reduce costs associated with poor surgical procedures and improve surgeons’ skills from different perspectives, not only ergonomics. Additionally, Multidisciplinary study in robotic surgery is becoming a relevant field, such as the human-robotic interaction. In this regard, real-time detection of the surgeon’s mental workload could provide helpful information to minimize the risk of human error during surgery. This was achieved by processing physiological signals from EEG and Eye-tracker sensors and then using machine learning techniques to estimate mental workload of the user. Leveraging the DVRK capabilities, real-time estimates of the user’s mental workload can be utilized for instance to activate autonomous systems that can assists the surgeon during bleeding events. Environmental elements such as noise, teamwork, calibration of the robot and proper tools are factors that should be considered when addressing studies related to human factors and robotic surgery. Even though, the surgery room is a complex environment to imitate from a laboratory, research in this field has brought considerable advantages for the surgeon – machine – patient system. To conclude, we want to provide feedback technology for surgeons reducing level of stress and perform better clinical results using sensing devices and the dvrk robot.

Da Vinci Research Kit: A tool for Surgical Robotics and Human Factors studies.