{"id":"https://openalex.org/W7140096687","doi":"https://doi.org/10.48550/arxiv.2603.19863","title":"MedQ-Engine: A Closed-Loop Data Engine for Evolving MLLMs in Medical Image Quality Assessment","display_name":"MedQ-Engine: A Closed-Loop Data Engine for Evolving MLLMs in Medical Image Quality Assessment","publication_year":2026,"publication_date":"2026-03-20","ids":{"openalex":"https://openalex.org/W7140096687","doi":"https://doi.org/10.48550/arxiv.2603.19863"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.19863","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.19863","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.19863","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5130388140","display_name":"Jiyao Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Liu, Jiyao","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101263879","display_name":"Junzhi Ning","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ning, Junzhi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078398555","display_name":"Wanying Qu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qu, Wanying","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085516582","display_name":"Lihao Liu","orcid":"https://orcid.org/0000-0002-8983-2342"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Lihao","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101539986","display_name":"Chenglong Ma","orcid":"https://orcid.org/0000-0002-1632-2480"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ma, Chenglong","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130352213","display_name":"Junjun He","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"He, Junjun","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5130409836","display_name":"Ningsheng Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Ningsheng","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5130388140"],"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.23280000686645508,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.23280000686645508,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.1152999997138977,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.08410000056028366,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.515999972820282},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5088000297546387},{"id":"https://openalex.org/keywords/modalities","display_name":"Modalities","score":0.4302000105381012},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.4147999882698059},{"id":"https://openalex.org/keywords/medical-imaging","display_name":"Medical imaging","score":0.41119998693466187},{"id":"https://openalex.org/keywords/data-quality","display_name":"Data quality","score":0.39739999175071716},{"id":"https://openalex.org/keywords/data-collection","display_name":"Data collection","score":0.3865000009536743},{"id":"https://openalex.org/keywords/image-quality","display_name":"Image quality","score":0.3824999928474426}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7513999938964844},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5375999808311462},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.515999972820282},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5088000297546387},{"id":"https://openalex.org/C2779903281","wikidata":"https://www.wikidata.org/wiki/Q6888026","display_name":"Modalities","level":2,"score":0.4302000105381012},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.4147999882698059},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.41119998693466187},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4066999852657318},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3977999985218048},{"id":"https://openalex.org/C24756922","wikidata":"https://www.wikidata.org/wiki/Q1757694","display_name":"Data quality","level":3,"score":0.39739999175071716},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38909998536109924},{"id":"https://openalex.org/C133462117","wikidata":"https://www.wikidata.org/wiki/Q4929239","display_name":"Data collection","level":2,"score":0.3865000009536743},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.3824999928474426},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.34380000829696655},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.33169999718666077},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.31450000405311584},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2825999855995178},{"id":"https://openalex.org/C1667742","wikidata":"https://www.wikidata.org/wiki/Q10927554","display_name":"Image retrieval","level":3,"score":0.2799000144004822},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.2793000042438507},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.2632000148296356},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.26170000433921814},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.25859999656677246},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.2558000087738037},{"id":"https://openalex.org/C174348530","wikidata":"https://www.wikidata.org/wiki/Q188635","display_name":"Bridging (networking)","level":2,"score":0.2506999969482422}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.19863","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.19863","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.19863","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.19863","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.5122084021568298,"display_name":"Quality Education"}],"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],"image":[1,93],"quality":[2,37],"assessment":[3],"(Med-IQA)":[4],"is":[5,42],"a":[6,74,91,111],"prerequisite":[7],"for":[8],"clinical":[9,33],"AI":[10],"deployment,":[11],"yet":[12],"multimodal":[13],"large":[14],"language":[15],"models":[16],"(MLLMs)":[17],"still":[18],"fall":[19],"substantially":[20],"short":[21],"of":[22,48,56],"human":[23,157],"experts,":[24],"particularly":[25],"when":[26],"required":[27],"to":[28,60,62,83,129,146,159],"provide":[29],"descriptive":[30,50],"assessments":[31],"with":[32,101,156,166],"reasoning":[34],"beyond":[35],"simple":[36],"scores.":[38],"However,":[39],"improving":[40],"them":[41],"hindered":[43],"by":[44,53,149],"the":[45,54,63,81,154],"high":[46],"cost":[47],"acquiring":[49],"annotations":[51,128,165],"and":[52,105,120,152],"inability":[55],"one-time":[57],"data":[58,76],"collection":[59],"adapt":[61],"model's":[64],"evolving":[65],"weaknesses.":[66],"To":[67],"address":[68],"these":[69,96],"challenges,":[70],"we":[71],"propose":[72],"MedQ-Engine,":[73],"closed-loop":[75],"engine":[77],"that":[78,140],"iteratively":[79],"evaluates":[80],"model":[82,145],"discover":[84],"failure":[85],"prototypes":[86,97],"via":[87],"data-driven":[88],"clustering,":[89],"explores":[90],"million-scale":[92],"pool":[94],"using":[95,162],"as":[98],"retrieval":[99],"anchors":[100],"progressive":[102],"human-in-the-loop":[103],"annotation,":[104],"evolves":[106],"through":[107],"quality-assured":[108],"fine-tuning,":[109],"forming":[110],"self-improving":[112],"cycle.":[113],"Models":[114],"are":[115],"evaluated":[116],"on":[117],"complementary":[118],"perception":[119],"description":[121],"tasks.":[122],"An":[123],"entropy-guided":[124],"routing":[125],"mechanism":[126],"triages":[127],"minimize":[130],"labeling":[131],"cost.":[132],"Experiments":[133],"across":[134],"five":[135],"medical":[136],"imaging":[137],"modalities":[138],"show":[139],"MedQ-Engine":[141],"elevates":[142],"an":[143],"8B-parameter":[144],"surpass":[147],"GPT-4o":[148],"over":[150,172],"13%":[151],"narrow":[153],"gap":[155],"experts":[158],"only":[160,163],"4.34%,":[161],"10K":[164],"more":[167],"than":[168],"4x":[169],"sample":[170],"efficiency":[171],"random":[173],"sampling.":[174]},"counts_by_year":[],"updated_date":"2026-03-24T06:04:31.470712","created_date":"2026-03-24T00:00:00"}
