From alternators to adaptive intelligence: Reimagining radiology education through integrated AI learning platforms

Radiology education continually evolves alongside advances in imaging technology, from film-based alternators to picture archiving and communication systems (PACS), voice recognition, and modern enterprise imaging environments. Artificial intelligence (AI) now represents the next major transformation in radiology training, with the potential to fundamentally reshape how residents acquire knowledge, develop reporting skills, receive feedback, and prepare for independent practice. While current AI educational efforts often focus on didactic literacy surrounding machine learning concepts, emerging opportunities extend far beyond passive instruction. This article describes a forward-looking framework for AI-integrated radiology education through two prototype platforms currently under development at our institution: RadCase and RadIntel. RadCase is an AI-driven educational ecosystem designed to curate institution-specific teaching files directly from PACS, integrate vetted educational resources, personalize learning pathways, and provide adaptive quiz-based reinforcement tailored to trainee level and performance. RadIntel is a complementary AI-enabled reporting intelligence platform designed to evaluate radiology reports using structured rubrics informed by American College of Radiology (ACR) Practice Parameters and Technical Standards (PP&TS) and Radiological Society of North America (RSNA) reporting guidance while simultaneously providing educational feedback, reporting analytics, and clinically oriented visualization tools. Together, these systems are designed to transition radiology education from static and asynchronous teaching models toward dynamic, individualized, data-driven learning environments. Beyond anticipated efficiency gains, these platforms are expected to improve resident engagement, standardize feedback, enhance oral board preparation, identify knowledge gaps, and support scalable education as clinical demands continue to increase. Prospective studies are planned to evaluate these anticipated benefits.