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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

Institutionalizing convergence education for medical artificial intelligence.

Tae In Park¹, Jongmo Seo², Hyung-Jin Yoon³

¹Department of Medicine, Seoul National University College of Medicine, Seoul, 03080 Republic of Korea.

Biomedical Engineering Letters

|November 24, 2025

Summary

Medical schools need integrated artificial intelligence (AI) training. Seoul National University College of Medicine developed a scalable, modular framework for AI convergence education, offering transferable insights for global institutions.

Keywords:

Convergence curriculum Digital health policy alignment Institutional governance Medical AI education Modular program design Transdisciplinary training

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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.