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<p>Despite the growing prominence of Artificial Intelligence (AI) in surgical practice, surgical residents and postgraduates receive limited formal training. A cross-sectional survey of 322 surgical residents and postgraduates from three Chinese medical universities assessed their knowledge, perceptions, and experience regarding AI in surgical practice. Among respondents, 76.7% reported prior experience with clinical AIrelated applications and generally recognized their value. However, self-reported preparedness to critically appraise AI and use it responsibly was modest or low. In particular, over 75% of respondents expressed limited confidence in evaluating model reliability and in identifying bias or other limitations. 85.7% supported the formal integration of AI into surgical training programs. On the other hand, 267 respondents reported that they had not received any formal AI training. Interest in AI-related topic differed significantly across training stages (χ<sup>2</sup> = 39.12, <i>p</i> &lt; 0.01). Postgraduate year 1 (PGY1) residents were most interested in learning the AI basics. PGY2 residents preferred topics about Human–AI collaboration and AI-assisted imaging analysis. PGY3 residents expressed interest in AI for research design and data analysis. These findings indicate a significant gap in AI education, highlighting the need for a stage-tailored and specialty-aware structured AI curriculum to prepare surgeons for the evolving world of healthcare technology.</p>

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Defining the artificial intelligence knowledge gap in surgery: experience and perspectives from surgical resident and postgraduate

  • 期刊论文

  • 发表日期：

    2026年5月21日

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  • Zhongshang Dai,
  • Zhuoyuan Chen,
  • Liang Weng,
  • Lin Guo,
  • Zhehao Dai

摘要

Despite the growing prominence of Artificial Intelligence (AI) in surgical practice, surgical residents and postgraduates receive limited formal training. A cross-sectional survey of 322 surgical residents and postgraduates from three Chinese medical universities assessed their knowledge, perceptions, and experience regarding AI in surgical practice. Among respondents, 76.7% reported prior experience with clinical AIrelated applications and generally recognized their value. However, self-reported preparedness to critically appraise AI and use it responsibly was modest or low. In particular, over 75% of respondents expressed limited confidence in evaluating model reliability and in identifying bias or other limitations. 85.7% supported the formal integration of AI into surgical training programs. On the other hand, 267 respondents reported that they had not received any formal AI training. Interest in AI-related topic differed significantly across training stages (χ2 = 39.12, p < 0.01). Postgraduate year 1 (PGY1) residents were most interested in learning the AI basics. PGY2 residents preferred topics about Human–AI collaboration and AI-assisted imaging analysis. PGY3 residents expressed interest in AI for research design and data analysis. These findings indicate a significant gap in AI education, highlighting the need for a stage-tailored and specialty-aware structured AI curriculum to prepare surgeons for the evolving world of healthcare technology.