The field of surgery is witnessing significant advancements in technology and training methods, driven by the need for improved precision, reduced variability, and enhanced patient outcomes. Recent developments have focused on leveraging artificial intelligence, computer vision, and machine learning to analyze surgical workflows, recognize patterns, and predict outcomes. These innovations have the potential to standardize training, accelerate competency acquisition, and advance data-informed surgical quality improvement. Noteworthy papers include the development of a consensus-based video-based assessment tool for workflow analysis in minimally invasive colorectal surgery, and an end-to-end AI system for surgical gesture sequence recognition and clinical outcome prediction. Additionally, a novel approach to generating natural-language surgical feedback has been proposed, which learns a surgical action ontology from real trainer-to-trainee transcripts and uses it to condition feedback generation.