医学
指导
模式(计算机接口)
人工智能
人机交互
管理
计算机科学
经济
作者
Ke Cheng,Shangdi Wu,Bing Peng,Xin Wang
标识
DOI:10.1097/js9.0000000000002713
摘要
Surgical coaching has emerged as an innovative educational strategy designed to enhance both the technical and non-technical competencies of surgeons through structured, individualized feedback. As minimally invasive surgical techniques continue to proliferate, video-based coaching has proven effective for skill refinement. However, its broader implementation remains limited due to a shortage of expert coaches and the labor-intensive nature of video review. Advances in artificial intelligence (AI), particularly in the field of computer vision (CV), present promising opportunities to optimize surgical coaching by automating video analysis and enabling scalable, data-driven feedback mechanisms. This study introduces SmartCoach, an AI-assisted surgical coaching program designed to support laparoscopic pancreatoduodenectomy (LPD)-a technically demanding procedure typically reserved for highly experienced surgeons. The program integrates an intelligent visualization system and structured post-operative debriefings to identify key performance issues and foster targeted improvement strategies. Preliminary survey data revealed limited awareness among participating surgeons regarding surgical coaching principles and the role of AI in surgical education. While most reported frequent use of operative videos for learning, they cited the lack of expert feedback and inefficiency as major barriers. The AI-driven coaching model seeks to address these challenges by providing real-time intraoperative assessments, automated identification of surgical steps, and enhanced scalability facilitated by 5 G-enabled communication technologies. Despite its promise, the implementation of AI-based coaching faces ethical, logistical, and cultural obstacles, including data privacy concerns and resistance to change among experienced surgeons. Nonetheless, the integration of AI into surgical coaching represents a transformative step toward improving operative performance, surgeon well-being, and patient outcomes, particularly in high-complex procedures where expert support is often limited.
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