Development and Applications of Artificial Intelligence in Medical Surgical Robotics

Abstract

With the continued advancement of minimally invasive surgery, precision medicine, and intelligent healthcare, medical surgical robots are rapidly evolving from conventional mechanical execution platforms into intelligent clinical assistance systems. The integration of artificial intelligence (AI) into perception, recognition, decision support, control, and feedback has significantly improved surgical precision, stability, and safety, while also creating new technical pathways for standardizing complex procedures, reducing operator burden, and expanding access to high-quality medical services. Based on a review of the system architecture of medical surgical robots and the theoretical foundations of Al, this paper systematically analyzes the roles of Al in preoperative planning, intraoperative navigation and operation assistance, and postoperative assessment and training feedback. Typical clinical scenarios, including laparoscopic surgery, orthopedics, neurosurgery, and vascular intervention, are further discussed to illustrate application value and technical characteristics. In addition, key challenges are examined, including data quality and heterogeneity, model generalization and real-time deployment, safety and reliability, ethical and regulatory constraints, and barriers to industrial translation. Corresponding development directions are proposed, including multimodal fusion, physician-led human-robot collaboration, data closed-loop learning with multicenter validation, standardized evaluation frameworks, and coordinated advancement among academia, industry, healthcare institutions, and regulators. Overall, Al is expected to continue driving medical surgical robotics from mechanical assistance toward intelligent assistance and higher-level collaborative operation.