Camille Gutierrez
Papers
5
Total Citations
58
H-Index
4
About
Camille Gutierrez is an emerging researcher at the intersection of surgical education, neurophysiology, and artificial intelligence, with a focused expertise in objective surgical skill assessment for robot-assisted surgery (RAS). Her work addresses a critical challenge in modern surgical training: developing reliable, data-driven methods to evaluate surgeon expertise beyond traditional subjective assessments. Gutierrez's most significant contributions involve leveraging electroencephalogram (EEG) signals, eye-tracking data, and advanced machine learning algorithms — including gradient boosting classifiers — to classify surgeons into distinct expertise tiers ranging from inexperienced to experienced. Her 2023 studies, collectively accumulating over 43 citations, demonstrated that physiological and visual metrics can reliably distinguish surgical skill levels during complex procedures such as cystectomy, hysterectomy, and vesico-urethral anastomosis. Her more recent 2024 work extends this framework to subtask-level classification and competency prediction, directly linking neurological and ocular biomarkers to standardized performance metrics like the Robotic Anastomosis Competency Evaluation (RACE). By combining neuroscience with surgical pedagogy, Gutierrez's research has meaningful implications for patient safety, surgical training standardization, and the future of AI-assisted medical education.
Research Focus
Key Achievements
Top Papers
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