Unimedvl: Unifying Medical Multimodal Understanding And Generation Through Observation-Knowledge-Analysis

Junzhi Ning, liwei, Cheng Tang, Jiashi Lin, Chenglong Ma, Chaoyang Zhang, Jiyao Liu, Ying Chen, Shujian Gao, Lihao Liu, YuandongPu, Huihui Xu, Chenhui Gou, Ziyan Huang, Yi Xin, qinqi, Joey Deng, Diping Song, Bin Fu, Guang Yang, Yuanfeng Ji, Tianbin Li, Yanzhou Su, Jin Ye, Shixiang Tang, Ming Hu, Junjun He

Unimedvl: Unifying Medical Multimodal Understanding And Generation Through Observation-Knowledge-Analysis: 7 upvotes on Hugging Face Daily Papers, #25 of 37 papers on 2025-10-22. Day-by-day upvote history.

Medical diagnostic applications require models that can process multimodal medical inputs (images, patient histories, lab results) and generate diverse outputs including both textual reports and visual content (annotations, segmentation masks, and images). Despite this need, existing medical AI systems disrupt this unified process: medical image understanding models interpret images but cannot generate visual outputs, while medical image generation models synthesize images but cannot provide textual explanations. This leads to gaps in data representation, feature integration, and task-level multimodal capabilities. To this end, we propose a multi-level framework that draws inspiration from diagnostic workflows through the Observation-Knowledge-Analysis (OKA) paradigm. Specifically, at the observation level, we construct UniMed-5M, a dataset comprising over 5.6M samples that reformat diverse unimodal data into multimodal pairs for foundational observation. At the knowledge level, we propose Progressive Curriculum Learning that systematically introduces medical multimodal knowledge. At the analysis level, we introduce UniMedVL, the first medical unified multimodal model for the simultaneous analysis of image understanding and generation tasks within a single architecture. UniMedVL achieves superior performance on five medical image understanding benchmarks, while matching specialized models in generation quality across eight medical imaging modalities. Crucially, our unified architecture enables bidirectional knowledge sharing: generation tasks enhance visual understanding features, demonstrating that integrating traditionally separate capabilities within a single medical framework unlocks improvements across diverse medical vision-language tasks. Code is available at https://github.com/uni-medical/UniMedVL.

Paper page on Hugging Face · arXiv

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