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<p>Medical AI education remains fragmented, specialty-skewed, and lacks longitudinal structure, particularly for generalist physicians. Through an integrative review of 23 peer-reviewed articles (2016–2025), we identified three structural gaps: short-term interventions without reinforcement, procedural-field bias, and consistent under-representation of the Affective domain. We present AI-PACE (Psychomotor, Affective, Cognitive, Embedded), a Bloom’s Taxonomy-grounded framework organizing AI competencies longitudinally across undergraduate, graduate, and continuing medical education.</p>

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AI-PACE: a framework for integrating AI into medical education

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月16日

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  • Scott P. McGrath,
  • Katherine K. Kim,
  • Karnjit Johl,
  • Haibo Wang,
  • Nick Anderson

摘要

Medical AI education remains fragmented, specialty-skewed, and lacks longitudinal structure, particularly for generalist physicians. Through an integrative review of 23 peer-reviewed articles (2016–2025), we identified three structural gaps: short-term interventions without reinforcement, procedural-field bias, and consistent under-representation of the Affective domain. We present AI-PACE (Psychomotor, Affective, Cognitive, Embedded), a Bloom’s Taxonomy-grounded framework organizing AI competencies longitudinally across undergraduate, graduate, and continuing medical education.