Abstract
With the profound progress in machine learning and deep learning methodologies the field of surgical data science has burgeoned over the last decade. To date, many proof-of-concept algorithms have emerged, capable of performing a variety of tasks. One area that is unique to surgical AI is in computer vision, which strives to understand images and videos from pixels to characterize anatomical structures, surgical tools, and surgical phases and, ultimately, provide surgical teams with decision support. Artificial intelligence applications have a useful role in the preoperative, intraoperative, and postoperative phases of care and have also had encouraging results in surgical education and robotic surgery. However, it is important to note that very few applications have made it to the patient’s bedside, and most are still in the preclinical phase with considerable obstacles that need to be overcome prior to implementation.
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