Scholarlink

<p>As artificial intelligence (AI) becomes increasingly central to modern healthcare, medical education must move beyond passive knowledge transfer and adopt a system-wide approach to convergence training. This narrative review shares a 5-year case study from Seoul National University College of Medicine (SNU Medicine), which developed a comprehensive, multi-level model for integrating AI into medical education. Instead of relying on pilot programs or piecemeal curriculum updates, SNU Medicine established a governance-driven, modular framework that includes institutional infrastructure, interdisciplinary teaching strategies, cross-campus credit integration, and alignment with national digital health policies. Based on this long-term case, we propose four key design principles—modularity, transdisciplinary alignment, infrastructure-curriculum coupling, and policy embeddedness—as a framework for creating scalable and sustainable convergence education in medical AI. While rooted in Korea’s unique policy environment, this model provides transferable insights for medical institutions worldwide, particularly those operating within public or policy-constrained environments.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Institutionalizing convergence education for medical artificial intelligence

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月1日

查看全文
  • Tae In Park,
  • Jongmo Seo,
  • Hyung-Jin Yoon,
  • Kyu Eun Lee

摘要

As artificial intelligence (AI) becomes increasingly central to modern healthcare, medical education must move beyond passive knowledge transfer and adopt a system-wide approach to convergence training. This narrative review shares a 5-year case study from Seoul National University College of Medicine (SNU Medicine), which developed a comprehensive, multi-level model for integrating AI into medical education. Instead of relying on pilot programs or piecemeal curriculum updates, SNU Medicine established a governance-driven, modular framework that includes institutional infrastructure, interdisciplinary teaching strategies, cross-campus credit integration, and alignment with national digital health policies. Based on this long-term case, we propose four key design principles—modularity, transdisciplinary alignment, infrastructure-curriculum coupling, and policy embeddedness—as a framework for creating scalable and sustainable convergence education in medical AI. While rooted in Korea’s unique policy environment, this model provides transferable insights for medical institutions worldwide, particularly those operating within public or policy-constrained environments.