League of Radiologists-an End-to-End AI Framework for Scalable and Gamified Radiology Education: A Pilot Implementation in Chest Radiography.
Authors
Affiliations (10)
Affiliations (10)
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street and 125 Nashua Street, Boston, MA, 02114, USA.
- Department of Electrical Engineering, Pontificia Universidad Católica de Chile, Marcoleta 367, Santiago, Región Metropolitana, 8320165, Chile.
- Department of Radiology, St. Vincent's Hospital Melbourne, 41 Victoria Parade, Fitzroy, VIC, Victoria, 3065, Australia.
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-Ro, Gangnam-Gu, Seoul, 06351, Republic of Korea.
- Department of Radiology, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-Ro, Seodaemun-Gu, Seoul, 03722, Republic of Korea.
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, 21 Sassoon Road, Pok Fu Lam, Hong Kong SAR, China.
- Medically Engineered Solutions in Healthcare Incubator (MESH IO), Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA.
- Mass General Brigham Innovation, Mass General Brigham, 399 Revolution Drive, Somerville, MA, 02145, USA.
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street and 125 Nashua Street, Boston, MA, 02114, USA. [email protected].
- Kempner Institute, Harvard University, 150 Western Avenue, Boston, MA, 02134, USA. [email protected].
Abstract
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.