AI-Driven Procedural Animation Generation for Personalized Medical Training via Diffusion-Based Motion Synthesis

Abstract

Medical education faces critical challenges in providing standardized, accessible, and personalized training content. Traditional manual animation creation requires extensive resources and time, limiting scalability. This paper presents an end-to-end framework that automatically generates high-quality, personalized medical training animations from clinical guidelines using diffusion-based motion synthesis. Our approach integrates natural language processing for medical concept extraction, knowledge graph construction for procedural representation, and a domain-adapted diffusion model with anatomical constraints. We introduce complexity-aware adaptive rendering techniques derived from game engine optimization to achieve real-time performance. Real-time cognitive load monitoring enables dynamic content adaptation. Comprehensive evaluation with 120 medical students demonstrates 23% improvement in learning outcomes and 35% reduction in training time. The system achieves 92 FPS on RTX 3070 and 72 FPS on Quest 2 while maintaining medical accuracy validated by board-certified surgeons (mean rating 4.6/5.0).