Background: Laparoscopic cholecystectomy (LC) is the gold standard for treating symptomatic gallstones but carries inherent risks like bile duct injury. While the critical view of safety (CVS) is advocated to mitigate bile duct injury, its real-world adoption is limited. Additionally, significant variations in surgeon performance impede procedural standardization, highlighting the need for a feasible, innovative, and effective training approach. The aim of this study is to develop an artificial intelligence (AI)-assisted coaching program for LC to enhance surgical education and improve surgeon’s performance. Materials and methods: The authors conducted a multicenter, randomized controlled trial from May 2022 to August 2023 to assess the impact of an AI-based coaching program, surgical coaching program, on novice performing LC. Surgeons and patients meeting specific inclusion criteria were randomly assigned to either a coaching group with AI-enhanced feedback or a self-learning group. The primary outcome was assessed using the Laparoscopic Cholecystectomy Rating Form, with secondary outcomes including surgical safety, efficiency, and adverse events. Statistical analyses were performed using SPSS, with significance set at a P-value less than 0.05. Results: Between May 2022 and August 2023, 22 surgeons were initially enrolled from 10 hospitals, with 18 completing the study. No demographic differences were noted between coaching and self-learning groups. In terms of surgical performance (Laparoscopic Cholecystectomy Rating Form scores), the coaching group showed significant improvement over time (31 to 40, P=0.008), outperforming the self-learning group by study end (40 vs 38, P=0.032). Significant improvements in CVS achievement were also noted in the coaching group (11% to 78%, P=0.021). Overall, the coaching program was well-received, outpacing traditional educational methods in both understanding and execution of CVS and participants in the intervention group expressed strong satisfaction with the program. Conclusions: The AI-assisted surgical coaching program effectively improved surgical performance and safety for novice surgeons in LC procedures. The model holds significant promise for advancing surgical education.

Background: The rate of bile duct injury (BDI) in laparoscopic cholecystectomy (LC) continues to be high due to low critical view of safety (CVS) achievement and the absence of an effective quality control system. The development of an intelligent system enables the automatic quality control of LC surgery and, eventually, the mitigation of BDI. This study aims to develop an intelligent surgical quality control system for LC and using the system to evaluate LC videos and investigate factors associated with CVS achievement. Materials and Methods: SurgSmart, an intelligent system capable of recognizing surgical phases, disease severity, critical division action, and CVS automatically, was developed using training datasets. SurgSmart was also applied in another multicenter dataset to validate its application and investigate factors associated with CVS achievement. Results: SurgSmart performed well in all models, with the critical division action model achieving the highest overall accuracy (98.49%), followed by the disease severity model (95.45%) and surgical phases model (88.61%). CVS I, CVS II, and CVS III had an accuracy of 80.64%, 97.62%, and 78.87%, respectively. CVS was achieved in 4.33% in the system application dataset. Additionally, the analysis indicated that surgeons at a higher hospital-level had a higher CVS achievement rate. However, there was still considerable variation in CVS achievement among surgeons in the same hospital. Conclusion: SurgSmart, the surgical quality control system, performed admirably in our study. Additionally, the system’s initial application demonstrated its broad potential for use in surgical quality control. Export