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arXiv · 2608.18937

MedUAG: Unified Understanding and Generation for Medical Multimodal Models

Zijie Meng·Yuncheng Zhang·Hualiang Wang·Yitian Tang·Xiaotang Gai·Chen Shen·Songtao Jiang·Shaosheng Cao·Jian Wu·Xian Wu·Zuozhu Liu
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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.

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Zijie Meng, Yuncheng Zhang, Hualiang Wang, Yitian Tang, Xiaotang Gai, Chen Shen, Songtao Jiang, Shaosheng Cao, Jian Wu, Xian Wu, Zuozhu Liu. 2026-08-19. MedUAG: Unified Understanding and Generation for Medical Multimodal Models. https://arxiv.org/abs/2608.18937

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