Academic

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

arXiv:2604.05738v1 Announce Type: new 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 (

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Han Jang, Junhyeok Lee, Heeseong Eum, Kyu Sung Choi · · 1 min read · 6 views
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arXiv:2604.05738v1 Announce Type: new 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.

Executive Summary

The article introduces MedLayBench-V, a groundbreaking large-scale multimodal benchmark designed to address the critical gap in patient-centered medical communication within Vision-Language Models (Med-VLMs). Unlike existing models trained primarily on professional literature, MedLayBench-V focuses on aligning expert-level diagnostic interpretations with layperson-understandable language. Utilizing a Structured Concept-Grounded Refinement (SCGR) pipeline, the authors integrate UMLS Concept Unique Identifiers (CUIs) to ensure semantic equivalence without risking hallucination. This benchmark provides a robust foundation for training and evaluating Med-VLMs to bridge the communication divide between clinicians and patients, thereby advancing equitable healthcare accessibility. The work is timely, addressing an overlooked yet pivotal challenge in AI-driven healthcare communication.

Key Points

  • ▸ Identification of a critical gap in Med-VLMs: their inability to communicate diagnostic findings in lay terms, despite achieving expert-level proficiency in interpretation.
  • ▸ Introduction of MedLayBench-V, the first large-scale multimodal benchmark focused on expert-lay semantic alignment, addressing the absence of such resources in the field.
  • ▸ Development of the Structured Concept-Grounded Refinement (SCGR) pipeline, which leverages UMLS CUIs to enforce semantic equivalence and mitigate hallucination risks in lay language generation.

Merits

Novelty and Timeliness

The article addresses a previously unmet need in the Med-VLM domain, filling a significant resource gap in patient-centered communication. The focus on lay-accessible medical image understanding is both innovative and urgently relevant to the evolving landscape of AI-driven healthcare.

Methodological Rigor

The SCGR pipeline introduces a robust framework for ensuring semantic equivalence between expert and lay descriptions. The integration of UMLS CUIs and micro-level entity constraints demonstrates a sophisticated approach to mitigating hallucination, a common pitfall in medical AI applications.

Scale and Practicality

MedLayBench-V is positioned as a large-scale benchmark, which is critical for training and evaluating next-generation Med-VLMs. Its multimodal nature ensures applicability across diverse diagnostic imaging modalities, enhancing its utility and generalizability.

Demerits

Limited Validation Scope

While the article outlines the construction of MedLayBench-V, there is limited discussion on the preliminary validation results or pilot testing of the benchmark. The absence of empirical evidence demonstrating the effectiveness of SCGR in real-world scenarios may raise questions about its practical applicability.

Potential Overhead in Implementation

The reliance on UMLS CUIs and structured refinement pipelines may introduce computational and operational complexities. Institutions without access to UMLS or sufficient computational resources may face challenges in adopting or replicating the benchmark.

Lack of Comparative Analysis

The article does not provide a comparative analysis against existing simplification methods or benchmarks. Without such benchmarks, it is difficult to assess the relative advantages or disadvantages of MedLayBench-V in practical applications.

Expert Commentary

This article represents a significant contribution to the field of medical AI, addressing a long-standing challenge in bridging the gap between expert-level diagnostic insights and patient-understandable communication. The introduction of MedLayBench-V and the SCGR pipeline underscores the importance of semantic alignment in Med-VLMs, particularly in an era where patient empowerment and shared decision-making are increasingly prioritized. The methodological rigor of the SCGR pipeline, with its emphasis on UMLS CUIs, provides a robust foundation for future research, though the lack of empirical validation in the article is a notable omission. The work also raises important questions about the scalability and generalizability of such benchmarks, particularly in resource-constrained settings. Furthermore, the ethical implications of AI-driven patient communication cannot be overstated; while the article focuses on technical solutions, the broader societal impact—such as trust in AI systems and the potential for over-reliance on automated explanations—warrants deeper exploration. Overall, this research sets a critical precedent for the development of patient-centered AI in healthcare, but its long-term success will depend on rigorous validation, stakeholder engagement, and alignment with regulatory frameworks.

Recommendations

  • ✓ Conduct pilot studies or preliminary validations of MedLayBench-V to demonstrate its effectiveness in real-world scenarios and across diverse patient populations.
  • ✓ Develop comparative analyses that benchmark MedLayBench-V against existing simplification methods or datasets to highlight its unique advantages and address potential criticisms of novelty.
  • ✓ Engage with regulatory bodies and healthcare organizations to establish standardized evaluation frameworks for AI-generated lay language, ensuring alignment with patient safety and ethical guidelines.
  • ✓ Explore the integration of MedLayBench-V into existing AI training pipelines and clinical workflows, assessing its impact on patient outcomes and clinician-patient interactions.
  • ✓ Expand the scope of the SCGR pipeline to include other domains requiring expert-lay communication, such as legal or technical fields, to assess its generalizability and scalability.

Sources

Original: arXiv - cs.CL

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