Evidence map›Paper›PMID 42010236›Full record

ArticleJournal of imaging informatics in medicine2026

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

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 and 7 more

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In one paragraph

Article in Journal of imaging informatics in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

17 authors.

Hyunji Kim *Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street and 125 Nashua Street, Boston, MA, 02114, USA.
Young-Tak Kim *Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street and 125 Nashua Street, Boston, MA, 02114, USA.
Saul Langarica *Department of Electrical Engineering, Pontificia Universidad Católica de Chile, Marcoleta 367, Santiago, Región Metropolitana, 8320165, Chile.
Kevin P FialkowskiDepartment of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street and 125 Nashua Street, Boston, MA, 02114, USA.
Jarrel C Y SeahDepartment of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street and 125 Nashua Street, Boston, MA, 02114, USA.
Jennifer S N TangDepartment of Radiology, St. Vincent's Hospital Melbourne, 41 Victoria Parade, Fitzroy, VIC, Victoria, 3065, Australia.
Kyoung Doo SongDepartment of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-Ro, Gangnam-Gu, Seoul, 06351, Republic of Korea.
Dae Chul JungDepartment of Radiology, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-Ro, Seodaemun-Gu, Seoul, 03722, Republic of Korea.
Kyongtae Tyler BaeDepartment of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, 21 Sassoon Road, Pok Fu Lam, Hong Kong SAR, China.
Rory L CochranDepartment of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street and 125 Nashua Street, Boston, MA, 02114, USA.
Marc D SucciDepartment of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street and 125 Nashua Street, Boston, MA, 02114, USA.
Shaunagh McDermottDepartment of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street and 125 Nashua Street, Boston, MA, 02114, USA.
Manisha BahlDepartment of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street and 125 Nashua Street, Boston, MA, 02114, USA.
Jeanne B AckmanDepartment of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street and 125 Nashua Street, Boston, MA, 02114, USA.
Michael H LevDepartment of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street and 125 Nashua Street, Boston, MA, 02114, USA.
Michael S GeeDepartment of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street and 125 Nashua Street, Boston, MA, 02114, USA.
Synho DoDepartment of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street and 125 Nashua Street, Boston, MA, 02114, USA. sdo@mgh.harvard.edu.ORCID http://orcid.org/0000-0001-6211-7050

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Artificial intelligenceEnd-to-End frameworkGamificationInteractive learningRadiology education

Identifiers

PMID42010236

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.