{"id":"https://openalex.org/W7161598499","doi":"https://doi.org/10.48550/arxiv.2605.16215","title":"Fully Open Meditron: An Auditable Pipeline for Clinical LLMs","display_name":"Fully Open Meditron: An Auditable Pipeline for Clinical LLMs","publication_year":2026,"publication_date":"2026-05-15","ids":{"openalex":"https://openalex.org/W7161598499","doi":"https://doi.org/10.48550/arxiv.2605.16215"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.16215","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.16215","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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.2605.16215","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5092831412","display_name":"Xavier Theimer-lienhard","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Theimer-Lienhard, Xavier","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136369621","display_name":"Mushtaha El-Amin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"El-Amin, Mushtaha","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134440491","display_name":"Fay Elhassan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Elhassan, Fay","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136406521","display_name":"Sahaj Vaidya","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vaidya, Sahaj","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136428972","display_name":"Victor Cartier-Negadi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cartier-Negadi, Victor","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134388493","display_name":"David Sasu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sasu, David","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134430354","display_name":"Lars Klein","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Klein, Lars","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5063796208","display_name":"Mary\u2010Anne Hartley","orcid":"https://orcid.org/0000-0002-8826-3870"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hartley, Mary-Anne","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/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.3546000123023987,"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"}},"topics":[{"id":"https://openalex.org/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.3546000123023987,"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"}},{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.20759999752044678,"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/T10350","display_name":"Electronic Health Records Systems","score":0.2013999968767166,"subfield":{"id":"https://openalex.org/subfields/3605","display_name":"Health Information Management"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.8485000133514404},{"id":"https://openalex.org/keywords/pipeline-transport","display_name":"Pipeline transport","score":0.6934000253677368},{"id":"https://openalex.org/keywords/resampling","display_name":"Resampling","score":0.47290000319480896},{"id":"https://openalex.org/keywords/protocol","display_name":"Protocol (science)","score":0.4560000002384186},{"id":"https://openalex.org/keywords/decision-support-system","display_name":"Decision support system","score":0.3903000056743622},{"id":"https://openalex.org/keywords/recipe","display_name":"Recipe","score":0.3864000141620636},{"id":"https://openalex.org/keywords/knowledge-base","display_name":"Knowledge base","score":0.36820000410079956},{"id":"https://openalex.org/keywords/clinical-practice","display_name":"Clinical Practice","score":0.34529998898506165}],"concepts":[{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.8485000133514404},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7059999704360962},{"id":"https://openalex.org/C175309249","wikidata":"https://www.wikidata.org/wiki/Q725864","display_name":"Pipeline transport","level":2,"score":0.6934000253677368},{"id":"https://openalex.org/C150921843","wikidata":"https://www.wikidata.org/wiki/Q1170431","display_name":"Resampling","level":2,"score":0.47290000319480896},{"id":"https://openalex.org/C2780385302","wikidata":"https://www.wikidata.org/wiki/Q367158","display_name":"Protocol (science)","level":3,"score":0.4560000002384186},{"id":"https://openalex.org/C107327155","wikidata":"https://www.wikidata.org/wiki/Q330268","display_name":"Decision support system","level":2,"score":0.3903000056743622},{"id":"https://openalex.org/C2778671685","wikidata":"https://www.wikidata.org/wiki/Q219239","display_name":"Recipe","level":2,"score":0.3864000141620636},{"id":"https://openalex.org/C4554734","wikidata":"https://www.wikidata.org/wiki/Q593744","display_name":"Knowledge base","level":2,"score":0.36820000410079956},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35600000619888306},{"id":"https://openalex.org/C2779974597","wikidata":"https://www.wikidata.org/wiki/Q28448986","display_name":"Clinical Practice","level":2,"score":0.34529998898506165},{"id":"https://openalex.org/C42058472","wikidata":"https://www.wikidata.org/wiki/Q810214","display_name":"Base (topology)","level":2,"score":0.3253999948501587},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3237999975681305},{"id":"https://openalex.org/C112930515","wikidata":"https://www.wikidata.org/wiki/Q4389547","display_name":"Risk analysis (engineering)","level":1,"score":0.30709999799728394},{"id":"https://openalex.org/C63527458","wikidata":"https://www.wikidata.org/wiki/Q5133829","display_name":"Clinical decision support system","level":3,"score":0.2969000041484833},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2955000102519989},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2937999963760376},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.29179999232292175},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.28450000286102295},{"id":"https://openalex.org/C2780535194","wikidata":"https://www.wikidata.org/wiki/Q309901","display_name":"Open data","level":2,"score":0.2824999988079071},{"id":"https://openalex.org/C2989179672","wikidata":"https://www.wikidata.org/wiki/Q6806500","display_name":"Clinical decision making","level":2,"score":0.2727999985218048},{"id":"https://openalex.org/C88626702","wikidata":"https://www.wikidata.org/wiki/Q1128903","display_name":"Continuation","level":2,"score":0.26750001311302185},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.26669999957084656},{"id":"https://openalex.org/C55166926","wikidata":"https://www.wikidata.org/wiki/Q2892946","display_name":"Oracle","level":2,"score":0.26660001277923584},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2632000148296356},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2581000030040741}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.16215","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.16215","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.16215","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.16215","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.4866446852684021}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Clinical":[0],"decision":[1],"support":[2],"systems":[3],"(CDSS)":[4],"require":[5],"scrutable,":[6],"auditable":[7],"pipelines":[8,38,219],"that":[9,39,216],"enable":[10],"rigorous,":[11],"reproducible":[12,79],"validation.":[13],"Yet":[14],"current":[15],"LLM-based":[16],"CDSS":[17],"remain":[18],"largely":[19],"opaque.":[20],"Most":[21],"\"open\"":[22],"models":[23,164],"are":[24,171],"open-weight":[25],"only,":[26],"releasing":[27],"parameters":[28],"while":[29],"withholding":[30],"the":[31,49,65,158],"data":[32,80],"provenance,":[33],"curation":[34],"procedures,":[35],"and":[36,82,85,103,121,134,205],"generation":[37],"determine":[40],"model":[41],"behavior.":[42],"Fully":[43,62],"Open":[44,63],"(FO)":[45],"models,":[46],"which":[47],"expose":[48],"complete":[50],"training":[51,76,83],"stack":[52],"end-to-end,":[53],"do":[54],"not":[55],"currently":[56],"exist":[57],"in":[58,200],"medicine.":[59],"We":[60,141,156],"introduce":[61],"Meditron,":[64],"first":[66],"fully":[67,217],"open":[68,218],"pipeline":[69,125],"for":[70],"building":[71],"LLM-CDSS,":[72],"comprising":[73],"a":[74,78,86,99,138,191],"clinician-audited":[75],"corpus,":[77],"construction":[81],"framework,":[84],"use-aligned":[87],"evaluation":[88],"protocol.":[89],"The":[90,124],"corpus":[91],"unifies":[92],"eight":[93],"public":[94],"medical":[95,188],"QA":[96,114],"datasets":[97],"into":[98],"normalized":[100],"conversational":[101],"format":[102],"expands":[104],"coverage":[105],"with":[106],"three":[107],"clinician-vetted":[108],"synthetic":[109],"extensions:":[110],"exam-style":[111],"QA,":[112],"guideline-grounded":[113],"derived":[115],"from":[116],"46,469":[117],"clinical":[118,122,149],"practice":[119],"guidelines,":[120],"vignettes.":[123],"enforces":[126],"system-wide":[127],"decontamination,":[128],"gold-label":[129],"resampling":[130],"of":[131,202],"teacher":[132],"generations,":[133],"end-to-end":[135],"validation":[136],"by":[137],"four-physician":[139],"panel.":[140],"evaluate":[142],"using":[143],"an":[144],"LLM-as-a-judge":[145,203],"protocol":[146],"over":[147,173,180,198],"expert-written":[148],"vignettes,":[150],"calibrated":[151],"against":[152],"204":[153],"human":[154],"raters.":[155],"apply":[157],"recipe":[159],"to":[160,184],"five":[161],"FO":[162,193],"base":[163,182],"(Apertus-70B/8B-Instruct,":[165],"OLMo-2-32B-SFT,":[166],"EuroLLM-22B/9B-Instruct).":[167],"All":[168],"MeditronFO":[169],"variants":[170],"preferred":[172,197],"their":[174],"bases.":[175],"Apertus-70B-MeditronFO":[176],"improves":[177],"+6.6":[178],"points":[179],"its":[181],"(47.2%":[183],"53.8%)":[185],"on":[186,208],"aggregate":[187],"benchmarks,":[189],"establishing":[190],"new":[192],"SoTA.":[194],"Gemma-3-27B-MeditronFO":[195],"is":[196],"MedGemma":[199],"58.6%":[201],"comparisons":[204],"outperforms":[206],"it":[207],"HealthBench":[209],"(58%":[210],"vs":[211],"55.9%).":[212],"These":[213],"results":[214],"show":[215],"can":[220],"achieve":[221],"state-of-the-art":[222],"domain-specific":[223],"performance":[224],"without":[225],"sacrificing":[226],"auditability":[227],"or":[228],"reproducibility.":[229]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-19T00:00:00"}