O022 Real-time artificial intelligence instructor vs expert instruction in teaching of expert level tumour resection skills – a randomized controlled trial

Abstract Introduction Competency-based approach in surgical training still lacks objective quantifiable methodologies to assess surgical technical skills and train residents, a limitation that can be addressed with the adaptation of artificial intelligence (AI). In this randomized controlled, the efficacy of learning by a real-time intelligent instruction system was compared to learning with in-person human instructor-mediated training. Methods The study was ethics approved. Ninety-eight medical students performed five virtually simulated brain tumour resections, randomly allocated into three feedback groups: (1) no-real-time feedback, (2) real-time intelligent instruction, and (3) in-person human instruction. The first task was considered as baseline performance, done with no feedback. Group-1 received expert benchmark feedback only after each procedure. Group-2 was 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. Group-3 was instructed by human instructors during the tasks. After each task, instructors summarized the areas of improvement and demonstrated how to expertly perform the tumour resections. Participant data in all tasks were scored by the AI system to assess learning. Results Students in Group-2 and Group-3 significantly improved their performance score by the third and second task, respectively (p<0.01, p=0.01), compared to the baseline performance. Group-2 achieved significantly higher scores than Group-3 in the final/fifth task (p<0.01). Conclusion AI-powered systems may increase efficiency in learning by providing objective, and action-oriented real-time feedback. Such systems may aid the shift towards competency-based surgical curricula.