  The absence of openly accessible data and specialized foundation models is a
major barrier for computational research in surgery. Toward this, (i) we
open-source the largest dataset of general surgery videos to-date, consisting
of 680 hours of surgical videos, including data from robotic and laparoscopic
techniques across 28 procedures; (ii) we propose a technique for video
pre-training a general surgery vision transformer (GSViT) on surgical videos
based on forward video prediction that can run in real-time for surgical
applications, toward which we open-source the code and weights of GSViT; (iii)
we also release code and weights for procedure-specific fine-tuned versions of
GSViT across 10 procedures; (iv) we demonstrate the performance of GSViT on the
Cholec80 phase annotation task, displaying improved performance over
state-of-the-art single frame predictors.
