Toward the intelligent operating room: Artificial intelligence and computer vision applications in surgery

More than 300 million major and minor surgical procedures are performed worldwide each year (1).During the perioperative period, over one-third of patients experience complications such as bleeding, organ injury, and infection, posing substantial challenges for patients, healthcare institutions, and health systems globally.Approximately half of these complications are classified as major, and many are considered potentially preventable, underscoring the urgent need for innovations that enhance procedural safety and surgical quality (2).The widespread adoption of minimally invasive and robotic techniques has fundamentally transformed modern surgical practice.Compared with open surgery, minimally invasive approaches are associated with faster recovery, reduced postoperative pain, shorter hospital stays, and lower complication rates, with outcomes improving further with increasing surgeon and institutional experience (3-5).These realities contribute to variability in surgical performance and highlight the need for scalable systems capable of supporting decision-making, technical precision, and intraoperative situational awareness.Concurrently, the operating room is undergoing a profound digital transformation.Fiber-optic cameras, robotic platforms, and advanced imaging systems now generate unprecedented volumes of high-resolution intraoperative video (6).Beyond serving as visualization tools, these technologies function as continuous data sources, enabling computational analysis of surgical activity (7).Artificial intelligence (AI), particularly through computer vision (CV) techniques capable of extracting structured information from visual data, offers new ways to analyze, quantify, and support surgical performance.This review focuses on the four areas with the most direct evidence of clinical translation: Computer-aided detection, instrument tracking, phase recognition, and video-based skill assessment.While many of these applications have initially focused on retrospective analysis, they increasingly point toward real-time intraoperative assistance, workflow optimization, and context-aware decision support.