{"paper":{"title":"MedLayBench-V: A Large-Scale Benchmark for Expert-Lay Semantic Alignment in Medical Vision Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"MedLayBench-V introduces the first large-scale benchmark for expert-lay semantic alignment in medical vision-language models.","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Han Jang, Heeseong Eum, Junhyeok Lee, Kyu Sung Choi","submitted_at":"2026-04-07T11:39:41Z","abstract_excerpt":"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-scal"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"we introduce MedLayBench-V, the first large-scale multimodal benchmark dedicated to expert-lay semantic alignment. ... 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.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the SCGR pipeline, by integrating UMLS CUIs with micro-level entity constraints, will enforce strict semantic equivalence between expert and lay descriptions without information loss, hallucination, or incomplete coverage of medical concepts.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"MedLayBench-V is the first large-scale multimodal benchmark for expert-lay semantic alignment in medical vision-language models, constructed via a Structured Concept-Grounded Refinement pipeline that uses UMLS CUIs to enforce equivalence.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"MedLayBench-V introduces the first large-scale benchmark for expert-lay semantic alignment in medical vision-language models.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"858c260b75078abb81f9f7286cb0499c5923b8459fadcca07545e98498fc3c4e"},"source":{"id":"2604.05738","kind":"arxiv","version":2},"verdict":{"id":"99e95158-bc04-4b93-b753-6146e0759c01","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-10T19:32:26.921463Z","strongest_claim":"we introduce MedLayBench-V, the first large-scale multimodal benchmark dedicated to expert-lay semantic alignment. ... 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.","one_line_summary":"MedLayBench-V is the first large-scale multimodal benchmark for expert-lay semantic alignment in medical vision-language models, constructed via a Structured Concept-Grounded Refinement pipeline that uses UMLS CUIs to enforce equivalence.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the SCGR pipeline, by integrating UMLS CUIs with micro-level entity constraints, will enforce strict semantic equivalence between expert and lay descriptions without information loss, hallucination, or incomplete coverage of medical concepts.","pith_extraction_headline":"MedLayBench-V introduces the first large-scale benchmark for expert-lay semantic alignment in medical vision-language models."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.05738/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":2,"snapshot_sha256":"36129d9fd642d8517c54fa8415c081880eb4090561df8f7b404ee469acf76040"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}