Year: 2025
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Baharoon, Mohammed, Raissi, Siavash, Jun, John S., Heintz, Thibault, Alabbad, Mahmoud, Alburkani, Ali, Kim, Sung Eun, Kleinschmidt, Kent, Alhumaydhi, Abdulrahman O., Alghamdi, Mohannad Mohammed G., Palacio, Jeremy Francis, Bukhaytan, Mohammed, Prudlo, Noah Michael, Akula, Rithvik, Chrisler, Brady, Galligos, Benjamin, Almutairi, Mohammed O., Alanazi, Mazeen Mohammed, Alrashdi, Nasser M., Hwang, Joel Jihwan, Jaliparthi, Sri Sai Dinesh, Nelson, Luke David, Nguyen, Nathaniel, Suryadevara, Sathvik, Kim, Steven, Mohammed, Mohammed F., Semenov, Yevgeniy R., Yu, Kun-Hsing, Aljouie, Abdulrhman, AlOmaish, Hassan, Rodman, Adam, Rajpurkar, Pranav
Institutions: Harvard University, Maastricht University, Mass General Brigham, King Abdulaziz Medical City, National Guard Health Affairs, Tufts University, Seoul National University Hospital, Saint Louis University, King Saud bin Abdulaziz University for Health Sciences, King Faisal Specialist Hospital & Research Centre, Massachusetts General Hospital, King Abdullah International Medical Research Center, Beth Israel Deaconess Medical Center
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: green
Funder: Harvard University
OpenAlex ID: W4415317928