In-Hwan Kim

Papers

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About

In-Hwan Kim is a leading figure in the advancement of minimally invasive spinal surgery, with a particular focus on biportal endoscopic techniques. His most-cited work, "Automatic tip detection of surgical instruments in biportal endoscopic spine surgery" (2021, 37 citations), exemplifies his pioneering integration of artificial intelligence with surgical precision. Kim’s primary research areas encompass computer-assisted surgery, medical image analysis, and the development of intelligent tools for spinal procedures. His major contribution lies in creating automated systems that enhance the accuracy and safety of endoscopic surgeries, reducing reliance on manual visualization and improving patient outcomes. By leveraging deep learning for real-time instrument tracking, Kim has addressed critical challenges in surgical workflow, such as instrument occlusion and depth perception. His work has garnered significant attention, with his top-cited paper serving as a cornerstone for subsequent studies in surgical AI. Beyond this, Kim is recognized for his efforts to bridge engineering and clinical practice, often collaborating with neurosurgeons to validate his technologies in operative settings. His research not only advances technical capabilities but also sets a foundation for future innovations in autonomous surgical assistance, making him a key contributor to the evolution of smart operating rooms.