MedLayBench-V: A Large-Scale Benchmark for Expert-Lay Semantic Alignment in Medical Vision Language Models
arXiv:2604.05738v2 Announce Type: replace 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 g
The proliferation of advanced medical AI necessitates clearer patient communication, driven by increasing public and regulatory scrutiny on AI transparency and accessibility.
This development addresses a critical gap in medical AI, improving patient understanding and trust by enabling models to communicate complex diagnostic findings in an accessible manner, which is crucial for adoption and ethical deployment.
The focus shifts beyond pure diagnostic accuracy to include the interpretability and explainability of AI outputs for lay audiences, impacting model design, training, and deployment in healthcare.
- · Patients
- · Healthcare providers
- · Medical AI developers focused on patient-centricity
- · Medical linguistics research
- · AI models lacking explainability features
- · Healthcare systems with poor patient communication
Med-VLMs will enhance their capabilities to translate complex expert medical language into understandable lay terms.
Improved patient comprehension of their medical conditions and treatment plans could lead to better adherence and health outcomes.
This could accelerate the integration of AI into direct patient interfaces, potentially reducing the burden on human clinicians for routine explanations.
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