RadGame: An AI-Powered Platform for Radiology Education

2025-09-16

AI Summary

Traditional radiology education relies on passive image interpretation or real-time mentor supervision, limiting scalability and timely, individualized feedback. To address this, we propose the first dual-module platform integrating large-scale public medical imaging datasets with AI-driven gamified instruction. The localization training module employs vision-language models and bounding-box alignment for real-time lesion localization assessment; the reporting module combines structured scoring criteria with AI-generated explanations to deliver interpretable, actionable feedback. Our key innovation lies in deeply embedding multimodal AI feedback into a closed-loop gamified learning framework. Experimental results demonstrate substantial improvements over conventional methods: lesion localization accuracy increases by 68 percentage points (85% vs. 17%), and structured report accuracy improves by 31 percentage points (35% vs. 4%). These gains significantly enhance both training efficiency and scalability.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Developing AI-powered gamified platform for radiology education
• Addressing limited immediate feedback in traditional radiology training
• Automating evaluation of localization and reporting skills using AI

Innovation

• AI gamification platform for radiology education
• Automated feedback using vision-language models
• Public dataset integration with structured scoring