Surgical Data Science and Informatics Lab

Overview

Dr. Thomas Shin is a surgeon-scientist specializing in minimally invasive, bariatric, and foregut surgery, whose research centers around applying computational methods to turn intraoperative and population-based surgical data into insight that answers questions across the surgical care pathway. Drawing on intraoperative data captured directly from robotic instruments, along with computer vision applied to operative video, he develops models that connect how an operation is performed to how patients do afterward. This work is supported by an NIH/NIBIB K08 award focused on robotic hernia repair. In parallel, he leads large-scale outcomes research that draws on population-based clinical data to compare surgical and medical therapies and to better stratify surgical risk. His research additionally explores how machine learning and large language models can be leveraged to optimize surgical education and improve patient communication. Beyond his research, Dr. Shin serves as an Associate Editor at npj Digital Medicine and holds leadership roles including co-chair of the ASMBS Emerging Technologies Committee, co-chair of the ASMBS Clinical Issues Committee, and membership on the IFSO Artificial Intelligence Task Force.