This research introduces a Retrieval-Augmented Generation (RAG) system that creates high-quality medical learning materials and integrates them with spaced repetition algorithms.

• Combines verified medical resources with LLM-generated explanations
• Optimized for Poland's State Specialization Examination (PES)
• Enhances knowledge retention while reducing cognitive overload
• Evaluated by medical professionals for accuracy and relevance

This innovation demonstrates how AI can transform medical education by creating personalized, reliable learning materials at scale—potentially improving physician training and patient outcomes.

Optimizing Retrieval-Augmented Generation of Medical Content for Spaced Repetition Learning