Institutionalizing convergence education for medical artificial intelligence

Area of Science:

• Medical Education
• Artificial Intelligence in Healthcare
• Digital Health

Background:

• Artificial intelligence (AI) is increasingly integral to healthcare.
• Medical education requires a shift towards comprehensive convergence training.
• Existing approaches often rely on pilot programs or fragmented curriculum changes.

Purpose of the Study:

• To present a 5-year case study of integrating AI into medical education at SNU Medicine.
• To propose a framework for scalable and sustainable convergence education in medical AI.
• To offer transferable insights for medical institutions globally.

Main Methods:

• A narrative review of a 5-year case study at Seoul National University College of Medicine.
• Development of a governance-driven, modular framework.
• Integration of institutional infrastructure, interdisciplinary teaching, and policy alignment.

Main Results:

• SNU Medicine established a multi-level model for AI integration in medical education.
• The model features a modular framework encompassing infrastructure, teaching, and policy.
• Four key design principles were identified: modularity, transdisciplinary alignment, infrastructure-curriculum coupling, and policy embeddedness.

Conclusions:

• A systematic, multi-level approach is crucial for effective AI integration in medical education.
• The proposed framework offers a scalable and sustainable model for convergence training.
• The model provides valuable insights for medical institutions worldwide, especially in policy-constrained settings.