Shen Zaiguo

Henan University of Chinese Medicine

Abstract：

To address clinical challenges such as limited operational precision, long physician learning curves, and insufficient intraoperative risk warnings in traditional surgeries, and to promote the evolution of surgical procedures toward precision, intelligence, and minimal invasiveness, this paper conducts research on the design and clinical validation of an artificial intelligence (AI)-assisted surgical robot system. Firstly, the core technologies of surgical robots and the application foundation of key AI technologies are reviewed. Combined with the surgical needs of multiple clinical departments and industry standards, the system design objectives and principles are defined. Subsequently, the overall system architecture is designed, dividing it into five subsystems: mechanical, control, AI-assisted, human-computer interaction, and data transmission and storage. The functions, interfaces, and core performance indicators of each module are determined. Detailed design of each subsystem follows, with a focus on developing the core algorithm modules of the AI-assisted subsystem to realize intelligent functions such as intraoperative tissue recognition, path planning, and risk warning. Finally, through a rigorous clinical validation protocol, ethical review, sample screening, equipment calibration, and personnel training are completed. Clinical validation is conducted, and the results are statistically analyzed, with optimization suggestions proposed for identified issues. The research results indicate that the designed AI-assisted surgical robot system meets clinical requirements in terms of operational precision, response speed, stability, clinical safety, and feasibility. It effectively reduces surgical trauma, shortens operation time, and decreases complication rates, providing a reliable intelligent auxiliary solution for surgery and possessing significant clinical application value and promotion prospects.

Key Words：

Artificial Intelligence; surgical robot; system design; clinical validation; precision medicine; human-machine collaboration