Max Robotics — Global Service Robot Directory + US Certification

About

Ambreen Shafqat is a pioneering researcher at the intersection of cognitive neuroscience, machine learning, and surgical robotics. Her work focuses on developing objective, data-driven models to assess and predict surgical expertise, cognitive workload, and performance in robot-assisted surgery (RAS). By integrating electroencephalogram (EEG) and eye-gaze data with advanced machine learning algorithms, Shafqat has created innovative classification models that can distinguish between novice and expert surgeons. Her most cited paper (22 citations) established a surgical skill level classification model using these multimodal physiological signals. She further expanded this work to predict cognitive workload and performance evaluation through functional brain network analysis (19 citations), and developed models for predicting learning rates in both fundamentals of laparoscopic surgery (FLS) and RAS tasks (9 citations). Most recently, she has applied her methodology to predict Robotic Anastomosis Competency Evaluation (RACE) metrics during vesico-urethral anastomosis, a critical step in robot-assisted radical prostatectomy. Shafqat’s research holds transformative potential for surgical training, offering objective, real-time feedback that could accelerate skill acquisition and improve patient outcomes.