Youngjin Moon
Papers
23
Total Citations
237
H-Index
9
About
Youngjin Moon is a prominent researcher at the intersection of medical robotics, artificial intelligence, and minimally invasive surgical systems. His work spans several interconnected domains, including robotic vascular intervention, deep reinforcement learning for catheter and guidewire navigation, surgical scene understanding, and rehabilitation robotics. Moon's most impactful contribution lies in applying deep reinforcement learning to autonomous medical device control. His 2021 paper on guidewire navigation in coronary artery phantoms has garnered 53 citations, demonstrating that AI-driven control can address the steep learning curve inherent in percutaneous coronary intervention. Building on this, his 2019 work on autonomous cardiac ablation catheter control (32 citations) established foundational methods for intelligent catheter steering. Together, these studies position Moon as a leading voice in autonomous intravascular robotics. Beyond navigation, Moon has made significant contributions to robotic system design, including a bi-motional roller cartridge vascular intervention device evaluated clinically (21 citations), novel biopsy end-effectors reducing radiation exposure (19 citations), and computer vision algorithms for improving surgeon situational awareness during laparoscopic procedures. His career reflects a coherent vision: reducing procedural risk, operator dependency, and X-ray exposure through intelligent robotic systems — research with direct, measurable patient safety implications.
Research Focus
Key Achievements
Top Papers
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- 7Novel Design of Master Manipulator for Robotic Catheter System11 citations · 2018
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