Human AI collaboration for unsupervised categorization of live surgical feedback.

ABSTRACT: Formative verbal feedback during live surgery is essential for adjusting trainee behavior and accelerating skill acquisition. Despite its importance, understanding optimal feedback is challenging due to the difficulty of capturing and categorizing feedback at scale. We propose a Human-AI Collaborative Refinement Process that uses unsupervised machine learning (Topic Modeling) with human refinement to discover feedback categories from surgical transcripts. Our discovered categories are rated highly for clinical clarity and are relevant to practice, including topics like "Handling and Positioning of (tissue)" and "(Tissue) Layer Depth Assessment and Correction [during tissue dissection]." These AI-generated topics significantly enhance predictions of trainee behavioral change, providing insi

SUBMITTER: Kocielnik R 

PROVIDER: S-EPMC11662073 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature