Shugo Kohno
Papers
2
Total Citations
19
H-Index
2
About
Shugo Kohno is a pioneering researcher at the intersection of artificial intelligence and surgical medicine, with a primary focus on developing AI-driven visual assistance systems for minimally invasive procedures. His most impactful work centers on semantic segmentation techniques that enable real-time, precise highlighting of anatomical structures during robot-assisted gastrectomy, a contribution that has garnered 16 citations since its publication in 2024. Kohno’s research directly addresses a critical challenge in surgery: enhancing intraoperative visualization to improve precision and reduce complications. Beyond technical innovation, he has demonstrated a commitment to surgical education, leading a cluster quasirandomized controlled trial in 2025 that validated the effectiveness of AI-based visualization tools for teaching anatomy—a study that has already attracted 3 citations. His work bridges the gap between computer vision and clinical practice, offering surgeons an augmented reality-like overlay of critical organs such as the pancreas. Kohno’s contributions are particularly notable for their translational impact, providing both immediate clinical utility and long-term educational benefits. As AI continues to reshape surgical workflows, his research stands as a model for how intelligent systems can augment human expertise in the operating room.
Research Focus
Key Achievements
Top Papers
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