Human AI collaboration for unsupervised categorization of live surgical feedback

IF 12.4 1区 医学 Q1 HEALTH CARE SCIENCES & SERVICES NPJ Digital Medicine Pub Date : 2024-12-20 DOI:10.1038/s41746-024-01383-3
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
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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.

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来源期刊
CiteScore
25.10
自引率
3.30%
发文量
170
审稿时长
15 weeks
期刊介绍: npj Digital Medicine is an online open-access journal that focuses on publishing peer-reviewed research in the field of digital medicine. The journal covers various aspects of digital medicine, including the application and implementation of digital and mobile technologies in clinical settings, virtual healthcare, and the use of artificial intelligence and informatics. The primary goal of the journal is to support innovation and the advancement of healthcare through the integration of new digital and mobile technologies. When determining if a manuscript is suitable for publication, the journal considers four important criteria: novelty, clinical relevance, scientific rigor, and digital innovation.
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