构建首个医学多模态通俗语义对齐基准，助力模型懂患者语言
MedLayBench-V: A Large-Scale Benchmark for Expert-Lay Semantic Alignment in Medical Vision Language Models
- 基于概念锚定的精修流程，确保专业与通俗表述语义一致
- 涵盖大规模多模态数据，支持临床专家与患者间的精准沟通评估
- 适合医疗AI、人机交互研究者，推动可解释性医学应用
医学视觉语言模型（Med-VLMs）已在影像诊断理解上达到专家水平，但其训练主要依赖专业文献，难以用通俗语言传达结果以支持以患者为中心的照护。尽管文本简化研究已有进展，却缺乏大规模多模态基准来推动医学图像的通俗化理解。为此，我们提出MedLayBench-V——首个专注于专家-通俗语义对齐的大规模多模态基准。不同于易产生幻觉的简单简化方法，本数据集通过结构化概念锚定精修（SCGR）流程构建，结合统一医学语言系统（UMLS）的概念唯一标识符（CUIs）与微观实体约束，严格保证语义等价性。MedLayBench-V为下一代能弥合临床专家与患者沟通鸿沟的Med-VLMs提供了可验证的训练与评估基础。
原文摘要 · Abstract (English)
Medical Vision-Language Models (Med-VLMs) have achieved expert-level proficiency in interpreting diagnostic imaging. However, current models are predominantly trained on professional literature, limiting their ability to communicate findings in the lay register required for patient-centered care. While text-centric research has actively developed resources for simplifying medical jargon, there is a critical absence of large-scale multimodal benchmarks designed to facilitate lay-accessible medical image understanding. To bridge this resource gap, we introduce MedLayBench-V, the first large-scale multimodal benchmark dedicated to expert-lay semantic alignment. Unlike naive simplification approaches that risk hallucination, our dataset is constructed via a Structured Concept-Grounded Refinement (SCGR) pipeline. This method enforces strict semantic equivalence by integrating Unified Medical Language System (UMLS) Concept Unique Identifiers (CUIs) with micro-level entity constraints. MedLayBench-V provides a verified foundation for training and evaluating next-generation Med-VLMs capable of bridging the communication divide between clinical experts and patients.
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