Hidekazu Iwaki
Papers
4
Total Citations
38
H-Index
3
About
Hidekazu Iwaki is a researcher at the forefront of AI-driven surgical robotics, specializing in surgical workflow analysis and prediction. His major contributions center on developing deep learning models that enable surgical assistance robots to not only understand but anticipate intraoperative events. His most impactful work, "SUPR-GAN: SUrgical PRediction GAN for Event Anticipation in Laparoscopic and Robotic Surgery" (2022), with 29 citations, pioneers a generative adversarial network that moves beyond identifying past surgical phases to predicting future actions—a critical step for real-time risk mitigation and decision support. Earlier studies, such as "Aggregating Long-Term Context for Learning Laparoscopic and Robot-Assisted Surgical Workflows" (2020–2021), established foundational methods for capturing extended temporal dependencies in surgical video, enabling robots to recognize complex workflows and provide timely warnings during high-risk phases. Notably, Iwaki’s career also includes work on intuitive interfaces for underfloor inspection robots (2008), demonstrating an early commitment to robotics for challenging environments. His research bridges computer vision, machine learning, and surgical robotics, with a clear trajectory toward enhancing patient safety and surgical precision through predictive AI.
Research Focus
Key Achievements
Top Papers
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