A Web-Based MRI Simulator with Knowledge-Based AI Assistance for Medical and Radiography Education

Abstract

Magnetic Resonance Imaging (MRI) education often suffers from limited access to physical scanners and the complexity of MRI parameter interdependencies. This paper presents a web-based MRI simulator integrated with a knowledge-based AI assistant to enhance medical and radiography training to bridge the gap between theoretical learning and practical experience in MRI procedures. The simulator offers medical and radiography students an intelligent, interactive platform accessible via any web browser. The simulator enables interactive MRI parameter adjustments and real-time imaging feedback, while the AI assistant employs structured knowledge representation and rule-based reasoning to provide personalized recommendations on parameter optimization, artifact reduction, and protocol selection. The system was developed using modern web technologies including Node.js as a backend solution, JavaScript-driven frontend to scalable with smooth navigation and MongoDB for database. The system’s intelligent assistant leverages domain-specific ontologies and rule-based reasoning to deliver personalized recommendations on parameter optimization, artifact reduction, and protocol selection. The usability testing with 30 medical, system development students and instructors demonstrated high satisfaction, achieving average usability and learning effectiveness scores of 85.1% and 84.7%, respectively. These results indicate the system’s potential to bridge theoretical learning and practical skills in MRI education. Future work will expand anatomical coverage and integrate deep learning to further enhance the AI assistant’s adaptability.

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