MEDLAYXPLAIN: Benchmarking the Expert-Lay Gap in Medical Vision-Language Models

• URL: http://arxiv.org/abs/2606.21194v1
• Date: Fri, 19 Jun 2026 08:06:09 GMT
• Title: MEDLAYXPLAIN: Benchmarking the Expert-Lay Gap in Medical Vision-Language Models
• Abstract summary: We introduce MedLayXPlain, the first large-scale benchmark and evaluation framework for Medical Lay Language Generation.<n>MedLayXPlain-122K provides 122,789 region-grounded samples across 8 imaging modalities from 12 publicly available source datasets.<n>We also introduce MedLayEval, a lightweight 3B evaluator distilled from a 27B verifier that scores expert-lay alignment across five clinically grounded attributes.
• Score: 1.7578235992860856
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Medical Vision-Language Models (Med-VLMs) achieve strong expert-level performance, yet their ability to generate patient-accessible descriptions remains underexplored. With the 21st Century Cures Act now mandating immediate patient access to diagnostic imaging results, evaluating whether Med-VLMs can bridge this Expert-Lay Gap is both urgent and clinically consequential for patient education and shared decision-making. To this end, we introduce MedLayXPlain, the first large-scale multimodal benchmark and evaluation framework for Medical Lay Language Generation (MLLG). MedLayXPlain-122K provides 122,789 region-grounded samples across 8 imaging modalities from 12 publicly available source datasets, each comprising a medical image with paired expert and lay captions anchored in a three-level Unified Medical Language System (UMLS) ontology hierarchy spanning 7 semantic groups, 43 semantic types, and 2,411 medical concepts. Lay captions are constructed via Hierarchical Ontology-Verified Refinement (HOVER), a three-step pipeline combining patient-centric vocabulary mapping, LLM-based constrained rewriting, and cross-model visual verification to enforce semantic equivalence while preventing hallucination. We further introduce MedLayEval, a lightweight 3B evaluator distilled from a 27B verifier that scores expert-lay alignment across five clinically grounded attributes, addressing the poor correlation between standard NLG metrics and clinical judgment. Benchmarking 33 VLMs on MedLayXPlain-122K reveals a systematic Expert-Lay Gap: medical VLMs achieve strong expert captioning but suffer significant lay-register degradation, while general-purpose VLMs produce more accessible language yet lack clinical precision, confirming that neither current paradigm adequately serves patient-facing communication.

Related papers

• Concept-Grounded Reasoning with Prompt-Driven Localization for Interpretable Structured Report Generation [66.62662330558906]
  CORAL is a multimodal framework that integrates spatial grounding and concept-level supervision into a unified reasoning process.<n>CORAL employs a prompt-driven medical segmentation model to localize lesions and predicts multi-class clinical attributes through a Concept Bottleneck module.<n>Experiments on BUS-CoT and IU X-ray datasets demonstrate consistent improvements in diagnostic accuracy, concept consistency, and report quality over strong general-purpose and medical MLLMs.
  arXiv  Detail & Related papers  (2026-09-14T10:21:23Z)
• A visual large language foundational model for medical image recognition using clinician-oriented social media [21.681667706513423]
  Large language models (LLMs) have demonstrated strong capabilities across diverse domains, showing considerable potential in medicine.<n>Here, we leverage de-identified medical images and expert shared commentaries on clinician-oriented social media.<n>By combining an advanced LLM with clinician-in-the-loop verification, we established a rigorous pipeline to construct ThoughtMed-1M.
  arXiv  Detail & Related papers  (2026-09-07T01:30:34Z)
• From Reasoning to Pixels: Grounded Medical Multimodal LLMs for VQA and Segmentation [24.67847881046532]
  textscMedREAL (textbfMedical textbfREasoning-driven textbfAnswering and textbfLocalization) is a unified framework that seamlessly aligns linguistic reasoning with spatial grounding.<n>textscMedREAL significantly outperforms state-of-the-arts, achieving 68.49% gIoU and 70.47% cIoU on benchmark evaluations.
  arXiv  Detail & Related papers  (2026-08-27T09:23:16Z)
• ClinFusion: A Vision-Centric Multimodal LLM System for Holistic Medical Understanding [67.57715989349471]
  Multimodal large language models (MLLMs) hold immense potential to revolutionize clinical practice, yet deploying them in the medical domain is fundamentally a vision-centric challenge.<n>We introduce ClinFusion, a vision-centric MLLM designed for holistic medical understanding.<n>We show that ClinFusion sets a new state-of-the-art across a comprehensive suite of 2D and 3D multimodal medical benchmarks.
  arXiv  Detail & Related papers  (2026-07-27T17:59:49Z)
• MedLayBench-V: A Large-Scale Benchmark for Expert-Lay Semantic Alignment in Medical Vision Language Models [2.0221473384338884]
  We introduce MedLayBench-V, the first large-scale multimodal benchmark dedicated to expert-lay semantic alignment.<n>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.
  arXiv  Detail & Related papers  (2026-04-07T11:39:41Z)
• DermoGPT: Open Weights and Open Data for Morphology-Grounded Dermatological Reasoning MLLMs [54.8829900010621]
  Multimodal Large Language Models (MLLMs) show promise for medical applications, yet progress in dermatology lags due to limited training data, narrow task coverage, and lack of clinically-grounded supervision.<n>We present a comprehensive framework to address these gaps.<n>First, we introduce DermoInstruct, a large-scale morphology-anchored instruction corpus comprising 211,243 images and 772,675 trajectories across five task formats.<n>Second, we establish DermoBench, a rigorous benchmark evaluating 11 tasks across four clinical axes: Morphology, Diagnosis, Reasoning, and Fairness, including a challenging subset of 3,600
  arXiv  Detail & Related papers  (2026-01-05T07:55:36Z)
• MedAlign: A Synergistic Framework of Multimodal Preference Optimization and Federated Meta-Cognitive Reasoning [52.064286116035134]
  We develop MedAlign, a framework to ensure visually accurate LVLM responses for Medical Visual Question Answering (Med-VQA)<n>We first propose a multimodal Direct Preference Optimization (mDPO) objective to align preference learning with visual context.<n>We then design a Retrieval-Aware Mixture-of-Experts (RA-MoE) architecture that utilizes image and text similarity to route queries to a specialized and context-augmented LVLM.
  arXiv  Detail & Related papers  (2025-10-24T02:11:05Z)
• Hulu-Med: A Transparent Generalist Model towards Holistic Medical Vision-Language Understanding [112.46150793476603]
  We introduce Hulu-Med, a transparent, generalist medical Vision-Language Model (VLM)<n>Hulu-Med is trained on a curated corpus of 16.7 million samples, spanning 12 major anatomical systems and 14 medical imaging modalities.<n>Hulu-Med surpasses existing open-source models on 27 of 30 benchmarks and outperforms proprietary systems such as GPT-4o on 16 benchmarks.
  arXiv  Detail & Related papers  (2025-10-09T17:06:42Z)
• TemMed-Bench: Evaluating Temporal Medical Image Reasoning in Vision-Language Models [54.48710348910535]
  Existing medical reasoning benchmarks primarily focus on analyzing a patient's condition based on an image from a single visit.<n>We introduce TemMed-Bench, the first benchmark designed for analyzing changes in patients' conditions between different clinical visits.
  arXiv  Detail & Related papers  (2025-09-29T17:51:26Z)
• Describe Anything in Medical Images [32.785523415007]
  We propose MedDAM, the first comprehensive framework leveraging large vision-language models for region-specific captioning in medical images.<n>MedDAM employs medical expert-designed prompts tailored to specific imaging modalities and establishes a robust evaluation benchmark.<n>This benchmark evaluates both MedDAM and other large vision-language models, focusing on clinical factuality through attribute-level verification tasks.
  arXiv  Detail & Related papers  (2025-05-09T05:45:31Z)
• Reinforced Correlation Between Vision and Language for Precise Medical AI Assistant [11.187690318227514]
  RCMed is a full-stack AI assistant that improves multimodal alignment in both input and output.<n>It achieves state-of-the-art precision in contextualizing irregular lesions and subtle anatomical boundaries.
  arXiv  Detail & Related papers  (2025-05-06T10:00:08Z)
• 3MDBench: Medical Multimodal Multi-agent Dialogue Benchmark [2.3011663397108078]
  3MDBench is an open-source framework for simulating and evaluating LVLM-driven telemedical consultations.<n> multimodal dialogue with internal reasoning improves F1 score by 6.5% over non-dialogue settings.<n> injecting predictions from a diagnostic convolutional neural network into the LVLM's context boosts F1 by up to 20%.
  arXiv  Detail & Related papers  (2025-03-26T07:32:05Z)
• Towards Evaluating and Building Versatile Large Language Models for Medicine [57.49547766838095]
  We present MedS-Bench, a benchmark designed to evaluate the performance of large language models (LLMs) in clinical contexts. MedS-Bench spans 11 high-level clinical tasks, including clinical report summarization, treatment recommendations, diagnosis, named entity recognition, and medical concept explanation. MedS-Ins comprises 58 medically oriented language corpora, totaling 13.5 million samples across 122 tasks.
  arXiv  Detail & Related papers  (2024-08-22T17:01:34Z)
• A Spectrum Evaluation Benchmark for Medical Multi-Modal Large Language Models [57.88111980149541]
  We introduce Asclepius, a novel Med-MLLM benchmark that assesses Med-MLLMs in terms of distinct medical specialties and different diagnostic capacities.<n>Grounded in 3 proposed core principles, Asclepius ensures a comprehensive evaluation by encompassing 15 medical specialties.<n>We also provide an in-depth analysis of 6 Med-MLLMs and compare them with 3 human specialists.
  arXiv  Detail & Related papers  (2024-02-17T08:04:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.