MedLayBench-V: A Large-Scale Benchmark for Expert-Lay Semantic Alignment in Medical Vision Language Models

TL;DR

MedLayBench-V is a large-scale multimodal benchmark designed to improve medical vision-language models' ability to communicate diagnostic findings in lay language, addressing a critical resource gap.

Contribution

We introduce MedLayBench-V, the first large-scale benchmark for expert-lay semantic alignment in medical vision-language models, constructed via a novel SCGR pipeline.

Findings

Dataset enforces strict semantic equivalence using UMLS CUIs.

Provides a verified foundation for training and evaluating lay-accessible medical models.

Addresses the lack of large-scale benchmarks for expert-lay medical image understanding.

Abstract

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.