Radhawk: An AI-driven Recommender for Pediatric Radiology Education.

RATIONALE AND OBJECTIVES: The unprecedented growth of technology and unfiltered information has led to cognitive overload and educational gaps in radiology. Radiologists and trainees often require "just-in-time" educational content while interpreting unfamiliar or infrequently encountered examinations, yet identifying reliable and appropriately targeted resources within the time constraints of the reading room can be challenging. This need is particularly relevant in pediatric radiology, where normal imaging appearances vary with age and development. Radhawk was developed as a scalable, artificial intelligence-driven intelligent tutoring system that delivers personalized, real-time educational resources to learners and radiologists, creating a precision curriculum tailored to each user's needs and clinical context. MATERIALS AND METHODS: Radhawk integrates with existing picture archiving and communication systems and a custom learning management system (RADiology's Intelligent Adaptive Learning). It utilizes natural language processing and machine learning algorithms to analyze imaging study information and provides results based on user preferences. The system's scalable architecture is built on Amazon Web Services, ensuring adaptability across multiple hospital systems. RESULTS: Radhawk successfully provides tailored recommendations for educational materials, including interactive courses, anatomical references, and case studies. Key features include: (1) Generation of personalized curricula based on learner performance and needs, (2) Presentation of age and gender-matched "normal" images for comparison, (3) Creation of user-specific heat maps to identify strengths and areas for improvement, and (4) Incorporation of expert subspecialist preferences to inform user choices. CONCLUSION: Radhawk replaces generic web searches with a knowledge-focused approach, providing instant access to high-quality, relevant resources. This precision education solution has the potential to improve diagnostic accuracy, enhance learning experiences, and address the shortage of specialized pediatric radiologists. Future plans include external testing across multiple pediatric radiology departments to validate Radhawk's scalability, acceptance, and value in real-world settings.