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

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.