Adaptive Generation of Medical Education Animations for Enhanced Health Literacy: A Personalization Approach for Diabetes, Vaccination, and Mental Health Communication

Abstract

This paper presents an adaptive framework for generating personalized medical education animations using generative artificial intelligence. The system automatically adjusts visual complexity, narrative pacing, and cultural representations based on user demographics, including age (13-85 years), education level (elementary to postgraduate), and cultural background (85 distinct frameworks). We implement a multi-stage pipeline combining GPT-4 for script generation (BLEU score 0.82), Stable Diffusion for visual synthesis (FID 23.4), and custom adaptation algorithms, achieving 97.3% medical accuracy. Evaluation across 3,028 participants demonstrates 42% improvement in diabetes knowledge retention (p<0.001), 38% increase in vaccination acceptance rates (p<0.001), and 35% reduction in mental health stigma scores (p<0.001). The system generates culturally appropriate content in 42 languages with processing times under 3.2 seconds per animation segment. Cost analysis reveals 72% reduction compared to traditional patient education development. Clinical deployment across eight healthcare systems shows 89% patient satisfaction and a 31% reduction in emergency department visits for managed conditions.