MedUAG: Unified Understanding and Generation for Medical Multimodal Models

2026-08-19

AI Summary

本文通过构建MedUAGCorpus数据集和MedUAGBench评估基准，开发了统一的医学多模态模型MedUAG，解决了医学领域缺乏综合训练、评估标准及验证模型的问题。

Abstract

Recent Multimodal Large Language Models (MLLMs) are rapidly evolving into unified understanding and generation (UAG) frameworks. However, extending these unified paradigms to the medical domain is hindered by: the absence of comprehensive training and evaluation benchmarks, and the lack of broadly validated unified medical model. To address these gaps, we present a comprehensive foundation for medical UAG. First, we construct MedUAGCorpus, the largest unified medical understanding and generation dataset to date, comprising over 6 million instances across 14 imaging modalities. Second, we introduce MedUAGBench, a systematic benchmark that expands medical generation evaluation to 12 diverse tasks under standardized protocols. Finally, leveraging these resources, we develop MedUAG, an end-to-end trained unified medical model. Extensive experiments demonstrate that MedUAG achieves strong performance across a wide array of understanding and generation tasks, establishing a competitive baseline and paving the way for next-generation medical multimodal systems.

Problem

• Multimodal Large Language Models
• Unified Understanding and Generation
• Medical Domain

Innovation

• Unified Understanding and Generation
• Medical Multimodal Models
• MedUAGCorpus
• MedUAGBench