Abstract
This study demonstrates Large Language models (LLMs) to assess and coach surgeons on their non-technical skills, traditionally evaluated through subjective and resource-intensive methods. Llama 3.1 and Mistral effectively analyzed robotic-assisted surgery transcripts, identified exemplar and non-exemplar behaviors, and autonomously generated structured coaching feedback to guide surgeons’ improvement. Our findings highlight the potential of LLMs as scalable, data-driven tools for enhancing surgical education and supporting consistent coaching practices.
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Details
1 Purdue University, Edwardson School of Industrial Engineering, West Lafayette, USA (GRID:grid.169077.e) (ISNI:0000 0004 1937 2197)
2 Indian Institute of Technology, Koita Centre for Digital Health, Bombay, India (GRID:grid.467228.d) (ISNI:0000 0004 1806 4045)
3 Indiana University, School of Medicine, Indianapolis, USA (GRID:grid.257413.6) (ISNI:0000 0001 2287 3919)