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<p>Traditional radiology education is constrained by a restricted apprenticeship model and a scarcity of datasets structured for building artificial intelligence (AI)-based radiology education systems. To address this problem, we developed a novel end-to-end framework for transforming vast clinical archives into scalable radiology education resources. The proposed framework converts static radiographic data into an interactive learning system through three integrated components. First, a multi-stage curation pipeline establishes a foundation of trustworthy cases suitable for radiology education from noisy public archives. Second, a large language model pipeline automatically generates a rich library of questions engineered to build core radiology reasoning skills. Finally, this content is deployed on an interactive, gamified platform that uses an adaptive algorithm to deliver a personalized and engaging learning experience. The curation pipeline distilled an initial pool of 493,785 images into a final dataset of 881 high-fidelity chest radiographs, from which the automated content generation pipeline produced 2305 multiple-choice questions. The system was implemented as the League of Radiologists, a publicly accessible platform (<a href="https://radontology.org">https://radontology.org</a>), demonstrating the feasibility of the proposed end-to-end architecture. A field demonstration resulted in 40 registered users and 68 unique examination sessions without technical failure, with 37.5% of active participants returning for multiple sessions. While currently focused on single finding chest radiographs, this study provides a practical and reproducible blueprint for implementing an AI-enabled adaptive radiology education platform using heterogeneous clinical imaging data. The described framework offers an extensible foundation for future development and evaluation of AI-driven educational systems in medical imaging.</p>

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League of Radiologists—an End-to-End AI Framework for Scalable and Gamified Radiology Education: A Pilot Implementation in Chest Radiography

  • 期刊论文

  • 发表日期：

    2026年4月20日

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  • Hyunji Kim,
  • Young-Tak Kim,
  • Saul Langarica,
  • Kevin P. Fialkowski,
  • Jarrel C. Y. Seah,
  • Jennifer S. N. Tang,
  • Kyoung Doo Song,
  • Dae Chul Jung,
  • Kyongtae Tyler Bae,
  • Rory L. Cochran,
  • Marc D. Succi,
  • Shaunagh McDermott,
  • Manisha Bahl,
  • Jeanne B. Ackman,
  • Michael H. Lev,
  • Michael S. Gee,
  • Synho Do

摘要

Traditional radiology education is constrained by a restricted apprenticeship model and a scarcity of datasets structured for building artificial intelligence (AI)-based radiology education systems. To address this problem, we developed a novel end-to-end framework for transforming vast clinical archives into scalable radiology education resources. The proposed framework converts static radiographic data into an interactive learning system through three integrated components. First, a multi-stage curation pipeline establishes a foundation of trustworthy cases suitable for radiology education from noisy public archives. Second, a large language model pipeline automatically generates a rich library of questions engineered to build core radiology reasoning skills. Finally, this content is deployed on an interactive, gamified platform that uses an adaptive algorithm to deliver a personalized and engaging learning experience. The curation pipeline distilled an initial pool of 493,785 images into a final dataset of 881 high-fidelity chest radiographs, from which the automated content generation pipeline produced 2305 multiple-choice questions. The system was implemented as the League of Radiologists, a publicly accessible platform (https://radontology.org), demonstrating the feasibility of the proposed end-to-end architecture. A field demonstration resulted in 40 registered users and 68 unique examination sessions without technical failure, with 37.5% of active participants returning for multiple sessions. While currently focused on single finding chest radiographs, this study provides a practical and reproducible blueprint for implementing an AI-enabled adaptive radiology education platform using heterogeneous clinical imaging data. The described framework offers an extensible foundation for future development and evaluation of AI-driven educational systems in medical imaging.