Introduction: Real-time artificial intelligence (AI) annotation of the surgical field has the potential to automatically extract information from surgical videos, helping to create a robust surgical atlas. This content can be used for surgical education and qualitative initiatives. We demonstrate the first use of AI in urologic robotic surgery to capture live surgical video and annotate key surgical steps and safety milestones in real-time. Methods: We conducted an educational symposium, which broadcasted two live procedures, a robotic-assisted radical prostatectomy (RARP) and robotic-assisted partial nephrectomy (RAPN). A surgical AI platform system (Theator, Palo Alto, CA) generated real-time annotations and identified operative safety milestones (Image 1). This was achieved through trained algorithms, conventional video recognition, and novel technology called Video Transfer Network which captures clips in full context, enabling automatic recognition and surgical mapping in real-time. Results: Real-time AI annotations for procedure #1, RARP, are found in Table 1. Safety milestone annotations included the apical safety maneuver and structures identification such as the external iliac vessels and the obturator nerve. Real-time AI annotations for procedure #2, RAPN, are found in Table 1. Safety milestones included deliberate views of structures such as the gonadal vessels and the ureter. AI annotated surgical events included intraoperative ultrasound and notable hemorrhage among others. Conclusions: Surgical intelligence successfully showcased real-time AI annotations of two separate urologic robotic procedures during live telecast for the first time. These annotations may provide the technological framework to send automatic notifications to clinical or operational stakeholders. This technology is a first step in real-time intraoperative decision support; leveraging big data to improve the quality of surgical care, potentially improve surgical outcomes and, support training and education. SOURCE OF Funding: N/A
