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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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League of Radiologists-an End-to-End AI Framework for Scalable and Gamified Radiology Education: A Pilot

Hyunji Kim1, Young-Tak Kim1, Saul Langarica2

  • 1Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street and 125 Nashua Street, Boston, MA, 02114, USA.

Journal of Imaging Informatics in Medicine
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Summary

This study introduces an AI-powered framework to create scalable radiology education resources from clinical data. The system transforms images into interactive learning modules, enhancing radiology training accessibility.

Keywords:
Artificial intelligenceEnd-to-End frameworkGamificationInteractive learningRadiology education

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Area of Science:

  • Medical Education
  • Artificial Intelligence in Radiology
  • Medical Imaging Informatics

Background:

  • Traditional radiology education relies on limited apprenticeship models.
  • Scarcity of structured datasets hinders the development of AI-based radiology education systems.
  • Existing clinical archives are vast but often unstructured for educational purposes.

Purpose of the Study:

  • To develop a novel end-to-end framework for transforming clinical archives into scalable radiology education resources.
  • To address the limitations of traditional radiology education through AI integration.
  • To create an interactive and personalized learning experience for radiology trainees.

Main Methods:

  • A multi-stage curation pipeline processed 493,785 images into 881 high-fidelity chest radiographs.
  • A large language model pipeline generated 2,305 multiple-choice questions from curated data.
  • Content was deployed on an interactive, gamified platform with an adaptive learning algorithm.

Main Results:

  • The framework successfully converted clinical data into an interactive learning system named League of Radiologists.
  • A field demonstration showed 40 registered users and 37.5% repeat engagement.
  • The system demonstrated the feasibility of an AI-enabled adaptive radiology education platform.

Conclusions:

  • The proposed framework provides a practical and reproducible blueprint for AI-driven radiology education.
  • The system is extensible for future development and evaluation using diverse medical imaging data.
  • This approach enhances radiology training by leveraging AI and interactive platforms.