Year: 2024
Type: article
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 insights beyond traditional manual categorization. For example, feedback on "Handling Bleeding" is linked to improved behavioral change. This work demonstrates the potential of AI to analyze surgical feedback at scale, informing better training guidelines and paving the way for automated feedback and cueing systems in surgery.
Source: npj Digital Medicine
Authors: Rafal Kocielnik, Cherine H. Yang, Runzhuo Ma, Steven Y. Cen, Elyssa Y. Wong, Timothy N. Chu, J. Everett Knudsen, Peter Wager, John Heard, Umar Ghaffar, Anima Anandkumar, Andrew J. Hung
Language: English
Subfield: Surgery
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,790
OpenAlex ID: W4405647189