Young‐Hak Kim
Papers
2
Total Citations
74
H-Index
2
About
Young-Hak Kim is a leading researcher in robotic vascular intervention and medical artificial intelligence, with a focus on enhancing the safety and precision of percutaneous coronary procedures. His most impactful work introduces a deep reinforcement learning framework for autonomous guidewire navigation in coronary artery phantoms, a breakthrough that addresses the critical challenge of non-linear control in flexible instrument steering—a skill that traditionally demands extensive training. This paper, with 53 citations, demonstrates how AI can learn complex manipulation tasks to improve procedural consistency. Complementing this, Kim developed a novel robotic vascular intervention assist device featuring a bi-motional roller cartridge structure, which reduces operator X-ray exposure and mitigates experience-dependent variability in clinical outcomes. This work, cited 21 times, has been validated through clinical evaluation, showing tangible improvements in procedural reliability. By integrating robotics and reinforcement learning, Kim’s contributions directly tackle two major limitations in interventional cardiology: the steep learning curve for guidewire control and the occupational hazards of radiation exposure. His research stands at the intersection of surgical robotics and intelligent automation, offering a pathway toward safer, more accessible vascular interventions.
Research Focus
Key Achievements
Top Papers
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- 2