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<p>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.</p>

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Feasibility of large language models for assessing and coaching surgeons’ non-technical skills

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月15日

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  • Marian Obuseh,
  • Sneha Singh,
  • Nicholas E. Anton,
  • Robin Gardiner,
  • Dimitrios Stefanidis,
  • Denny Yu

摘要

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.