Matias Mose

Papers

Total Citations

H-Index

About

Matias Mose is at the forefront of surgical data science, specializing in the intersection of deep learning and robot-assisted surgery (RAS). His research focuses on developing automated systems for surgical action recognition and skills assessment, aiming to transform how surgical trainees are evaluated and trained. In his most-cited work, "Video-based robotic surgical action recognition and skills assessment on porcine models using deep learning," Mose demonstrates a novel approach to objectively assess surgeon proficiency by analyzing video data from robotic procedures. This contribution addresses a critical gap in surgical education, where subjective evaluation methods have long been the norm. With 14 citations in a rapidly evolving field, his work is gaining traction among researchers and clinicians alike. Mose’s achievements include advancing the use of convolutional neural networks for real-time feedback in surgical training, a step toward safer and more consistent patient outcomes. His research not only pushes the boundaries of AI in medicine but also offers practical tools for improving surgical expertise, making him a rising voice in the movement toward data-driven, personalized surgical education.