Engineering AI for Surgery: Progress, Challenges, and Opportunities

Surgical Data Science aims to improve the quality of interventional healthcare and its value through the capture and analysis of data. The operating room has long been siloed from in-depth review and analysis, but recent advances in computer vision and surgical robotics offer the promise of improved understanding and prediction of intraoperative events. However, challenges remain around collecting and annotating intraoperative data and modeling complex physiological phenomena and workflows during surgery. While engineers and surgeons have long worked together to advance clinical care, the advent of new sensors, surgical modalities, and emerging Ai techniques presents new opportunities for collaboration. We will discuss the translational challenges that remain, as well as opportunities for interdisciplinary collaboration to ensure new surgical technologies are not only scientifically rigorous but also clinically impactful.