{"id":"https://openalex.org/W7151824854","doi":"https://doi.org/10.48550/arxiv.2604.05738","title":"MedLayBench-V: A Large-Scale Benchmark for Expert-Lay Semantic Alignment in Medical Vision Language Models","display_name":"MedLayBench-V: A Large-Scale Benchmark for Expert-Lay Semantic Alignment in Medical Vision Language Models","publication_year":2026,"publication_date":"2026-04-07","ids":{"openalex":"https://openalex.org/W7151824854","doi":"https://doi.org/10.48550/arxiv.2604.05738"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.05738","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.05738","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.05738","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5125384753","display_name":"Han Jang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jang, Han","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133211570","display_name":"Junhyeok Lee","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lee, Junhyeok","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128715726","display_name":"Heeseong Eum","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Eum, Heeseong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5006749140","display_name":"Kyu Sung Choi","orcid":"https://orcid.org/0000-0002-5175-3307"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Choi, Kyu Sung","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.786300003528595,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.786300003528595,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.0544000007212162,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.03220000118017197,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/bridging","display_name":"Bridging (networking)","score":0.7696999907493591},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5544999837875366},{"id":"https://openalex.org/keywords/unified-medical-language-system","display_name":"Unified Medical Language System","score":0.5329999923706055},{"id":"https://openalex.org/keywords/identifier","display_name":"Identifier","score":0.4666000008583069},{"id":"https://openalex.org/keywords/equivalence","display_name":"Equivalence (formal languages)","score":0.3995000123977661},{"id":"https://openalex.org/keywords/semantic-mapping","display_name":"Semantic mapping","score":0.39169999957084656},{"id":"https://openalex.org/keywords/limiting","display_name":"Limiting","score":0.3783000111579895},{"id":"https://openalex.org/keywords/semantic-gap","display_name":"Semantic gap","score":0.37380000948905945}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7811999917030334},{"id":"https://openalex.org/C174348530","wikidata":"https://www.wikidata.org/wiki/Q188635","display_name":"Bridging (networking)","level":2,"score":0.7696999907493591},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5544999837875366},{"id":"https://openalex.org/C69505689","wikidata":"https://www.wikidata.org/wiki/Q455338","display_name":"Unified Medical Language System","level":2,"score":0.5329999923706055},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5123999714851379},{"id":"https://openalex.org/C154504017","wikidata":"https://www.wikidata.org/wiki/Q853614","display_name":"Identifier","level":2,"score":0.4666000008583069},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.45980000495910645},{"id":"https://openalex.org/C2780069185","wikidata":"https://www.wikidata.org/wiki/Q7977945","display_name":"Equivalence (formal languages)","level":2,"score":0.3995000123977661},{"id":"https://openalex.org/C2775955345","wikidata":"https://www.wikidata.org/wiki/Q7449071","display_name":"Semantic mapping","level":2,"score":0.39169999957084656},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.3783000111579895},{"id":"https://openalex.org/C86034646","wikidata":"https://www.wikidata.org/wiki/Q474311","display_name":"Semantic gap","level":4,"score":0.37380000948905945},{"id":"https://openalex.org/C37926939","wikidata":"https://www.wikidata.org/wiki/Q7449061","display_name":"Semantic equivalence","level":4,"score":0.35659998655319214},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3564999997615814},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.34709998965263367},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.3375999927520752},{"id":"https://openalex.org/C534262118","wikidata":"https://www.wikidata.org/wiki/Q177719","display_name":"Medical diagnosis","level":2,"score":0.32829999923706055},{"id":"https://openalex.org/C90312973","wikidata":"https://www.wikidata.org/wiki/Q7449052","display_name":"Semantic data model","level":2,"score":0.32679998874664307},{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.3248000144958496},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.2750999927520752},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.2612999975681305},{"id":"https://openalex.org/C2780878386","wikidata":"https://www.wikidata.org/wiki/Q1659648","display_name":"Visual language","level":2,"score":0.25049999356269836}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.05738","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.05738","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.05738","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.05738","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.7338685989379883,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Medical":[0,107],"Vision-Language":[1],"Models":[2],"(Med-VLMs)":[3],"have":[4],"achieved":[5],"expert-level":[6],"proficiency":[7],"in":[8,27],"interpreting":[9],"diagnostic":[10],"imaging.":[11],"However,":[12],"current":[13],"models":[14],"are":[15],"predominantly":[16],"trained":[17],"on":[18],"professional":[19],"literature,":[20],"limiting":[21],"their":[22],"ability":[23],"to":[24,56,76],"communicate":[25],"findings":[26],"the":[28,70,133],"lay":[29],"register":[30],"required":[31],"for":[32,42,124],"patient-centered":[33],"care.":[34],"While":[35],"text-centric":[36],"research":[37],"has":[38],"actively":[39],"developed":[40],"resources":[41],"simplifying":[43],"medical":[44,59],"jargon,":[45],"there":[46],"is":[47,89],"a":[48,92,121],"critical":[49],"absence":[50],"of":[51,131],"large-scale":[52,72],"multimodal":[53,73],"benchmarks":[54],"designed":[55],"facilitate":[57],"lay-accessible":[58],"image":[60],"understanding.":[61],"To":[62],"bridge":[63],"this":[64],"resource":[65],"gap,":[66],"we":[67],"introduce":[68],"MedLayBench-V,":[69],"first":[71],"benchmark":[74],"dedicated":[75],"expert-lay":[77],"semantic":[78,102],"alignment.":[79],"Unlike":[80],"naive":[81],"simplification":[82],"approaches":[83],"that":[84],"risk":[85],"hallucination,":[86],"our":[87],"dataset":[88],"constructed":[90],"via":[91],"Structured":[93],"Concept-Grounded":[94],"Refinement":[95],"(SCGR)":[96],"pipeline.":[97],"This":[98],"method":[99],"enforces":[100],"strict":[101],"equivalence":[103],"by":[104],"integrating":[105],"Unified":[106],"Language":[108],"System":[109],"(UMLS)":[110],"Concept":[111],"Unique":[112],"Identifiers":[113],"(CUIs)":[114],"with":[115],"micro-level":[116],"entity":[117],"constraints.":[118],"MedLayBench-V":[119],"provides":[120],"verified":[122],"foundation":[123],"training":[125],"and":[126,139],"evaluating":[127],"next-generation":[128],"Med-VLMs":[129],"capable":[130],"bridging":[132],"communication":[134],"divide":[135],"between":[136],"clinical":[137],"experts":[138],"patients.":[140]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-04-09T00:00:00"}
