At a glance
ClinicalIndex Comparison RecordStandardized by ClinicalIndex from the ClinicalTrials.gov record · verify against the source.
Effect of Artificial Intelligence-Augmented Human Instruction on Surgical Simulation Performance: A Randomized Controlled Trial
In Brief
A clinical study evaluating Expert instruction using AI tutor script and AI-augmented personalized expert instruction for Surgical Education. Completed, enrolled 88 participants across 1 site.
Detailed Summary
At the Neurosurgical Simulation and Artificial Intelligence Learning Centre, we seek to provide surgical trainees with innovative technologies that allow them to improve their surgical technical skills in risk-free environments, potentially improving patient operative outcomes. The Intelligent Continuous Expertise Monitoring System (ICEMS), a deep learning application that assesses and trains neurosurgical technical skill and provides continuous intraoperative feedback, is one such technology that may improve surgical education. In this randomized controlled trial, medical students from four Quebec universities will be blinded and randomized to one of three groups (one control and two experimental). Group 1 (control) will be provided with verbal AI tutor feedback based on the ICEMS error detection. Group 2 will be tutored by a human instructor who will receive ICEMS error data and deliver verbal instruction using the same words as the ICEMS. Group 3 will be tutored by a human instructor who will be provided with ICEMS data and will then deliver personalized feedback. The aim of this study is to determine how the method of delivery of verbal surgical error instruction influences trainee technical skill acquisition and transfer. Evaluating trainee responses to AI instructor verbal feedback as compared to feedback from human instructors will allow for further development, testing, and optimization of the ICEMS and other AI tutoring systems.
Study Details
Timeline
Interventions
Expert instructor assigned to tutor this group will receive error detection data from the ICEMS. They will also be provided with a list of commands that the ICEMS uses. When the system detects an error in a student's performance for a given metric, the instructor must deliver this command using the exact wording provided by the ICEMS.
Expert instructor assigned to tutor this group will receive error detection data from the ICEMS. When the system detects an error in a student's performance for a given metric, the instructor will have the freedom to personalize and contextualize feedback without restriction to ICEMS wording.