Real-Time Artificial Intelligence Instruction in Comparison to Human Expert Instruction in Surgical Technical Skills Teaching - a Randomized Controlled Trial

Abstract Aim Surgical simulators equipped with artificial intelligence (AI) systems provide risk-free training on realistically simulated complex patient cases, objective performance assessment, and tailored error avoidance feedback. This study compared the efficacy of real-time AI instruction with human instructor-mediated training in surgical technical skills teaching. Method Medical students (n = 98) were randomly allocated into three feedback groups. Everyone repeated a virtual brain tumour resection five times. The first repetition was done without receiving feedback to assess their baseline performance. Students in Group 1 did not receive real-time feedback, and they were shown visual feedback only after each procedure based on expert benchmarks. Students in Group 2 were instructed in real-time by the AI system. After each task, the students were shown their error-video clips generated by this system alongside the expert-level demonstrations relating to each error. Students in Group 3 were instructed by human instructors during the tasks. After each task, instructors summarized the areas of improvement and demonstrated correction techniques. All participant data was scored using a composite-score. Statistical analysis was conducted to compare learning from the first to the last task repetition. Results Students in Group 2 and Group 3 significantly improved in the performance score by the third and second repetition, respectively (p<0.01, p = 0.01) compared to their baseline performance. Students in Group 2 achieved significantly higher scores than those in Group 3 in the fifth repetition (p<0.01). Conclusions Artificial intelligence systems may provide competency-based curricula in surgical training with efficient surgical technical skills teaching methodologies.