RadGame: An AI-Powered Platform for Radiology Education

Mohammed Baharoon, Siavash Raissi, John S Jun, Thibault Heintz, Mahmoud Hussain Alabbad, Ali M. Alburkani, Sung Eun Kim, Kent Kleinschmidt, Abdulrahman O. Alhumaydhi, Mohannad Mohammed G Alghamdi, Jeremy Francis Palacio, Mohammed Bukhaytan, Noah Michael Prudlo, Rithvik Akula, Brady Chrisler, Benjamin Galligos, Mohammed O Almutairi, Mazeen Mohammed Alanazi, Nasser M Alrashdi, Joel Jihwan Hwang et al. (12 additional authors not shown)

Keywords: Radiology education, Gamification, Medical AI, Report generation, Localization

Track: Proceedings

Abstract: We introduce $\textbf{RadGame}$, 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 $\textit{RadGame Localize}$, 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 $\textit{RadGame Report}$, 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 $\textit{68\}$% improvement in localization accuracy compared to $\textit{17}$% with traditional passive methods and a $\textit{31\}$% improvement in report-writing accuracy compared to $\textit{4\}$% 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.

General Area: Applications and Practice

Specific Subject Areas: Other (Use Sparingly), Medical Imaging

Data And Code Availability: Yes

Ethics Board Approval: Yes

Entered Conflicts: I confirm the above

Anonymity: I confirm the above

Submission Number: 123