Physiological signals can predict errors in robot-assisted surgery

In robot-assisted surgery simulations, physiological measurements significantly distinguished between error and non-error intervals. Analysis of EKG and EEG data from 57 participants revealed that high performers consistently displayed different physiological responses compared to low performers. Classification models accurately identified errors with 85.7% precision and performance groups with 96.3% accuracy. This research suggests that noninvasive physiological monitoring could serve as a real-time error detection and training tool in surgical settings, enhancing patient safety.