RadGame: An AI-Powered Platform for Radiology Education
Abstract
We introduce RadGame, an AI-powered gamified platform for radiology education that targets two core skills: localizing findings and generating reports. Traditional radiology training is based on passive exposure to cases or active practice with real-time input from supervising radiologists, limiting opportunities for immediate and scalable feedback. RadGame addresses this gap by combining gamification with large-scale public datasets and automated, AI-driven feedback that provides clear, structured guidance to human learners. In RadGame Localize, players draw bounding boxes around abnormalities, which are automatically compared to radiologist-drawn annotations from public datasets, and visual explanations are generated by vision-language models for user missed findings. In RadGame Report, players compose findings given a chest X-ray, patient age and indication, and receive structured AI feedback based on radiology report generation metrics, highlighting errors and omissions compared to a radiologist's written ground truth report from public datasets, producing a final performance and style score. In a prospective evaluation, participants using RadGame achieved a 68% improvement in localization accuracy compared to 17% with traditional passive methods and a 31% improvement in report-writing accuracy compared to 4% with traditional methods after seeing the same cases. RadGame highlights the potential of AI-driven gamification to deliver scalable, feedback-rich radiology training and reimagines the application of medical AI resources in education.
Cite
@article{arxiv.2509.13270,
title = {RadGame: An AI-Powered Platform for Radiology Education},
author = {Mohammed Baharoon and Siavash Raissi and John S. Jun and Thibault Heintz and Mahmoud Alabbad and Ali Alburkani and Sung Eun Kim and Kent Kleinschmidt and Abdulrahman O. Alhumaydhi and Mohannad Mohammed G. Alghamdi and Jeremy Francis Palacio and Mohammed Bukhaytan and Noah Michael Prudlo and Rithvik Akula and Brady Chrisler and Benjamin Galligos and Mohammed O. Almutairi and Mazeen Mohammed Alanazi and Nasser M. Alrashdi and Joel Jihwan Hwang and Sri Sai Dinesh Jaliparthi and Luke David Nelson and Nathaniel Nguyen and Sathvik Suryadevara and Steven Kim and Mohammed F. Mohammed and Yevgeniy R. Semenov and Kun-Hsing Yu and Abdulrhman Aljouie and Hassan AlOmaish and Adam Rodman and Pranav Rajpurkar},
journal= {arXiv preprint arXiv:2509.13270},
year = {2026}
}
Comments
ML4H Version