MedAI Hub: A Multimodal Medical Data Platform with Evolutionary Image Enhancement and Graph-Driven Literature Retrieval

摘要

We present MedAI Hub, an integrated multimodal medical platform designed to bridge clinical practice and research by transforming patient-doctor interactions into structured scientific data. This platform supports comprehensive management of multimodal medical records-including clinical notes, medical images, and patient-reported outcomes-while implementing privacy-preserving data sharing mechanisms. Building upon this infrastructure, we introduce two novel AI-driven modules:(1) ITERATE (Image-Text Enhancement, Retrieval, and Alignment): An evolutionary algorithm inspired by Visual Genome that optimizes medical image-text alignment through iterative cross-modal refinement. Leveraging LLM-guided ''DNA evolution'' and multimodal feedback, ITERATE enhances ultrasound image quality for diagnostic tasks, achieving 3.5-7% accuracy gains on ScienceQA and ARC-Easy benchmarks.(2) MedQuery: A graph-driven literature retrieval system that constructs multimodal knowledge graphs from medical literature (text, figures, tables). By aligning PubMed documents with complex clinical queries through semantic relationship modeling, it achieves >90% answer quality win rates and 13-36% accuracy improvements on PubMedQA and MedInquiry datasets. MedAI Hub demonstrates that synergistic integration of clinical data platforms with evolutionary vision-language optimization and multimodal knowledge graphs significantly advances medical AI capabilities, enabling more accurate diagnostics and research insights. The platform and algorithms are publicly available to accelerate innovation in medical AI.