Purpose or Case Report: Pediatric radiology presents unique educational challenges, requiring trainees to master complex imaging patterns across varying patient ages and developmental stages. We developed an innovative learning platform powered by large language models (LLMs) to address these challenges through personalized, adaptive instruction. Our approach aims to bridge the gap between traditional teaching methods and the need for consistent, scalable feedback in pediatric imaging education.
Methods & Materials: We developed an online learning platform utilizing the GPT-4o-mini large language model to process and evaluate free-text radiological interpretations. The platform creates individual learning profiles that adaptively deliver cases according to competency level. The system employs a structured schema-based approach where the LLM maps trainee responses to a set of predefined diagnostic features. This enables automated, immediate feedback by comparing structured interpretations against expert-verified correct answers. The LLM processes free-text responses by analyzing natural language input, mapping key findings to a predetermined structured schema, and generating real-time feedback based on comparison with expert-verified answers.
Results: The platform implements a point-based progression system where users can earn up to 5 points for each correct finding. Participants advance to higher competency levels upon reaching 50-point thresholds, resulting in incrementally more challenging cases. A prototype module focusing on stroke detection is currently available at Adaptirad.com, demonstrating the system's capability to handle complex radiological interpretations. The platform's architecture allows for straightforward adaptation to pediatric radiology applications.
Conclusions: Our LLM-powered learning platform represents a significant advancement in radiology education technology. The system's ability to process natural language inputs reduces cognitive transference, creating a learning experience that closely mirrors clinical practice. The platform's adaptable architecture enables easy implementation across various pediatric radiology applications. Initial testing with users of varying expertise levels has shown promising results. Future work will focus on developing pediatric-specific modules and analyzing user performance data to optimize the educational intervention.