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Related Concept Videos

Health Information Technology and Healthcare Information System01:30

Health Information Technology and Healthcare Information System

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Updated: May 24, 2026

Digital Home-Monitoring of Patients after Kidney Transplantation: The MACCS Platform
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Published on: April 12, 2021

CODE MEDI: A Collaborative Hackathon for Health Informatics Education.

Hyeon Ji Kim1, Sona Ha1, Jaerang Go2

  • 1Department of Data Sciences Convergence, Dong-A University, Republic of Korea.

Studies in Health Technology and Informatics
|May 23, 2026
PubMed
Summary
This summary is machine-generated.

The CODE MEDI hackathon successfully integrated medical and computer science students, fostering AI skills and mutual understanding. This interdisciplinary approach proved effective for medical AI education.

Keywords:
Artificial IntelligenceEducationHackathonInterdisciplinary learning

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Last Updated: May 24, 2026

Digital Home-Monitoring of Patients after Kidney Transplantation: The MACCS Platform
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Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
11:21

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

Area of Science:

  • Medical Education
  • Artificial Intelligence
  • Software Engineering

Background:

  • Medical education increasingly requires integration of artificial intelligence (AI) and software (SW) principles.
  • Interdisciplinary collaboration between medical and computer science fields is crucial for developing healthcare innovations.

Purpose of the Study:

  • To evaluate the CODE MEDI program, a two-day hackathon designed to promote interdisciplinary learning between medical and computer science students.
  • To assess the effectiveness of a hackathon model for fostering AI and software skills in a medical context.

Main Methods:

  • A two-day hackathon (CODE MEDI) involving 36 students (21 medical, 15 engineering/AI) in mixed teams.
  • Development of healthcare-related prototype models.
  • Post-event surveys and word-cloud analysis of open-ended feedback.

Main Results:

  • High participant satisfaction (8.34 ± 1.75/10) and agreement on teamwork, mentoring, and interdisciplinary learning.
  • Key collaboration challenge identified as differing technical terminology (46.8%).
  • Frequent themes in feedback included knowledge, learning, medical, and experience.

Conclusions:

  • The CODE MEDI program effectively fostered mutual understanding and practical AI application skills.
  • Hackathon-based learning is a valuable model for integrating AI and software education into medical curricula.