The field of surgical technology and automation is rapidly advancing, with a focus on developing innovative solutions to improve surgical outcomes, reduce complications, and enhance patient care. Recent developments have centered around the use of deep learning models, computer vision, and robotics to automate various aspects of surgical procedures, such as tool presence detection, C-arm positioning, and catheter navigation. These advancements have the potential to improve the accuracy, efficiency, and safety of surgical interventions. Noteworthy papers in this area include: Automated C-Arm Positioning via Conformal Landmark Localization, which presents a pipeline for autonomous C-arm navigation, and DINO-CVA, a multimodal goal-conditioned vision-to-action model for autonomous catheter navigation. EndoCIL, a class-incremental learning framework for endoscopic image classification, also shows promise for clinical scalability and deployment. Overall, these developments demonstrate the significant progress being made in surgical technology and automation, and highlight the potential for these innovations to transform the field of surgery.