MedUAG: Unified Understanding and Generation for Medical Multimodal Models

• URL: http://arxiv.org/abs/2608.18937v1
• Date: Wed, 19 Aug 2026 14:05:22 GMT
• Title: MedUAG: Unified Understanding and Generation for Medical Multimodal Models
• Abstract summary: We present a comprehensive foundation for medical unified understanding and generation (UAG)<n>First, we construct MedUAGCorpus, the largest unified medical understanding and generation dataset to date.<n>Second, we introduce MedUAGBench, a systematic benchmark that expands medical generation evaluation to 12 diverse tasks under standardized protocols.<n>Third, we develop MedUAG, an end-to-end trained unified medical model.
• Score: 26.03938719382972
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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