# Institutionalizing convergence education for medical artificial intelligence

## TL;DR

This paper presents a comprehensive model for integrating AI into medical education through a long-term case study at a Korean medical school.

## Contribution

The paper introduces a scalable and sustainable framework for convergence education in medical AI with four key design principles.

## Key findings

- SNU Medicine developed a multi-level model for AI integration in medical education over five years.
- The model includes institutional infrastructure, interdisciplinary teaching, and policy alignment.
- The proposed framework is transferable to other medical institutions globally.

## Abstract

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.