Stephen Chang
National University Hospital, National University of Singapore
Papers
7
Total Citations
113
H-Index
6
About
Stephen Chang is a pioneering researcher at the intersection of medical robotics, surgical automation, and minimally invasive procedures. His work has made significant contributions to the development of intelligent robotic systems for cancer treatment and surgical training, with a particular focus on radiofrequency ablation (RFA) and laparoscopic surgery. Chang's most influential contribution, "A Robotic System for Overlapping Radiofrequency Ablation in Large Tumor Treatment" (2010, 55 citations), demonstrated how surgical robots could dramatically improve the accuracy and efficiency of treating large liver tumors — a historically challenging clinical problem. Building on this foundation, he advanced image-guided robotic systems that integrate medical imaging with precise needle navigation, enabling single-incision RFA procedures that minimize patient trauma. Beyond tumor treatment, Chang has made notable strides in surgical education technology, developing robot trainers that learn from expert surgeons through demonstration and statistical modeling, effectively allowing novice surgeons to practice laparoscopic techniques guided by machine-replicated expertise. His work on mixed reality guidance further bridges computational planning with real-world surgical execution. With over 100 cumulative citations, Chang's research represents a meaningful contribution to making complex surgeries safer, more reproducible, and more teachable through intelligent robotic systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2Mechanism of a Learning Robot Manipulator for Laparoscopic Surgical Training16 citations · 2012
- 3
- 4
- 5
- 6
- 7Learning laparoscopic surgery by imitation using robot trainer4 citations · 2011