Poster Sat, Apr 25, 2026 • 6:30 AM – 9:00 AM PDT Pavilion 3 P3-#1116

U2-BENCH: Benchmarking Large Vision-Language Models on Ultrasound Understanding

Anjie Le ⋅ Henan Liu ⋅ Yue Wang ⋅ Zhenyu Liu ⋅ Rongkun Zhu ⋅ Taohan Weng ⋅ Jinze Yu ⋅ Boyang Wang ⋅ Yalun Wu ⋅ Kaiwen Yan ⋅ Quanlin Sun ⋅ Meirui Jiang ⋅ Jialun Pei ⋅ Siya Liu ⋅ Haoyun Zheng ⋅ Zhoujun Li ⋅ J. Alison Noble ⋅ Jacques Souquet ⋅ Xiaoqing Guo ⋅ Manxi Lin ⋅ Hongcheng Guo

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Abstract

Ultrasound is a widely-used imaging modality critical to global healthcare, yet its interpretation remains challenging due to its varying image quality on operators, noises, and anatomical structures. Although large vision-language models (LVLMs) have demonstrated impressive multimodal capabilities across natural and medical domains, their performance on ultrasound remains largely unexplored. We introduce U2-BENCH, the first comprehensive benchmark to evaluate LVLMs on ultrasound understanding across classification, detection, regression, and text generation tasks. U2-BENCH aggregates 7,241 cases spanning 15 anatomical regions and defines 8 clinically inspired tasks, such as diagnosis, view recognition, lesion localization, clinical value estimation, and report generation, across 50 ultrasound application scenarios. We evaluate 23 state-of-the-art LVLMs, both open- and closed-source, general-purpose and medical-specific. Our results reveal strong performance on image-level classification, but persistent challenges in spatial reasoning and clinical language generation. U2-BENCH establishes a rigorous and unified testbed to assess and accelerate LVLM research in the uniquely multimodal domain of medical ultrasound imaging.