RadGame: An AI-Powered Platform for Radiology Education
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| author | 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 |
| author_facet | 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 |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_13270 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | RadGame: An AI-Powered Platform for Radiology Education 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 Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | RadGame: An AI-Powered Platform for Radiology Education |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2509.13270 |