Papers
18
Total Citations
206
H-Index
9
About
Steven Cen is a researcher at the forefront of surgical robotics, artificial intelligence, and surgical education, with a portfolio that bridges clinical outcomes and cutting-edge technology. His work centers on developing objective, data-driven tools to assess and improve surgeon performance, with particular emphasis on robot-assisted procedures. Cen has made significant contributions to the field by demonstrating that automated performance metrics derived from robotic instrument kinematics can predict critical patient outcomes, including urinary continence recovery and surgical margin status following robot-assisted radical prostatectomy — findings that have garnered over 50 citations combined and carry direct implications for patient safety and surgical training standards. A recurring theme in his research is the translation of machine learning and AI into actionable surgical feedback systems. His pilot study on AI-driven video feedback for robotic suturing (27 citations) and the development of validated assessment frameworks such as EASE and DART underscore his commitment to standardizing surgical competency evaluation. Cen has also explored the relationship between cognitive workload, technical skill, and surgical error, offering a nuanced understanding of performance under pressure. Through multi-institutional collaborations, his research shapes how future surgeons are trained, assessed, and ultimately certified in an increasingly robotic surgical landscape.
Research Focus
Key Achievements
Top Papers
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- 9Development of a Classification System for Live Surgical Feedback10 citations · 2023
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