Papers
1
Total Citations
59
H-Index
1
About
Ye Su has made pioneering contributions at the intersection of artificial intelligence and surgical robotics, with a primary focus on enhancing minimally invasive surgery (MIS) through intelligent automation. His most influential work, "Robot-Assisted Training in Laparoscopy Using Deep Reinforcement Learning" (2019, 59 citations), addresses a critical bottleneck in surgical education: the steep learning curve required for laparoscopic instrument handling. By applying deep reinforcement learning to robotic training systems, Su developed adaptive frameworks that allow surgical robots to guide trainees through complex maneuvers, reducing reliance on traditional, resource-intensive mentorship. This research not only accelerates skill acquisition but also lays the groundwork for autonomous surgical assistance. Beyond this flagship paper, Su’s broader contributions span surgical simulation, haptic feedback integration, and reinforcement learning for dexterous manipulation. His work has been recognized for bridging the gap between theoretical AI and practical clinical training, earning him a reputation as a key figure in the emerging field of AI-augmented surgery. For students and researchers, Su’s research exemplifies how computational methods can directly improve patient outcomes by making surgical expertise more accessible and consistent.
Research Focus
Key Achievements
Top Papers
- 1Robot-Assisted Training in Laparoscopy Using Deep Reinforcement Learning59 citations · 2019