[Application of artificial intelligence for surgical skill assessment and quality control in minimally invasive...

Minimally invasive surgery has become an important approach for treating complex surgical conditions. As such surgical procedures are often associated with high risks and technical complexities, the surgeon's skills, efficiency of team collaboration, and capacities for emergency response all significantly affect the surgical outcomes. Establishing a robust surgical skill assessment and quality control system is therefore of vital importance to reduce the risks and enhance perioperative safety. In this context, the conventional methods for surgical skill assessment and quality control (e.g., subjective scoring, postoperative analysis, etc.) appear insufficient due to their inconsistent standards, time and labor intensity, and lack of real-time feedback. In recent years, artificial intelligence (AI), particularly computer vision-based deep learning technology, has demonstrated immense potential in surgical motion recognition, workflow phase analysis, and intraoperative safety monitoring, and has propelled the development of surgical skill assessment and quality control toward automation, objectivity, and precision. Herein the authors review the latest research progress in the application of AI in minimally invasive surgical skill assessment and quality control, focusing on AI-based skill assessment and quality control algorithm models. This review also addresses model generalization, privacy protection, multicenter applicability, and future clinical applications of intelligent precision surgery.