{"id":"https://openalex.org/W7154085936","doi":"https://doi.org/10.48550/arxiv.2604.09009","title":"Robust by Design: A Continuous Monitoring and Data Integration Framework for Medical AI","display_name":"Robust by Design: A Continuous Monitoring and Data Integration Framework for Medical AI","publication_year":2026,"publication_date":"2026-04-10","ids":{"openalex":"https://openalex.org/W7154085936","doi":"https://doi.org/10.48550/arxiv.2604.09009"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.09009","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.09009","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.2604.09009","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5029561767","display_name":"Mohammad Daouk","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Daouk, Mohammad","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125755577","display_name":"Jan Ulrich Becker","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Becker, Jan Ulrich","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025494644","display_name":"Neeraja Kambham","orcid":"https://orcid.org/0000-0003-1136-3686"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kambham, Neeraja","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133531818","display_name":"Anthony Chang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chang, Anthony","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133502366","display_name":"Chandra Mohan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mohan, Chandra","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133515475","display_name":"Hien Van Nguyen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Van Nguyen, Hien","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5029561767"],"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/T10862","display_name":"AI in cancer detection","score":0.2110999971628189,"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"}},"topics":[{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.2110999971628189,"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/T11438","display_name":"Retinal Imaging and Analysis","score":0.17110000550746918,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.07129999995231628,"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/mahalanobis-distance","display_name":"Mahalanobis distance","score":0.6101999878883362},{"id":"https://openalex.org/keywords/data-integration","display_name":"Data integration","score":0.5},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.4171000123023987},{"id":"https://openalex.org/keywords/cross-entropy","display_name":"Cross entropy","score":0.39070001244544983},{"id":"https://openalex.org/keywords/performance-metric","display_name":"Performance metric","score":0.37720000743865967},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.3711000084877014},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.36550000309944153},{"id":"https://openalex.org/keywords/automation","display_name":"Automation","score":0.3612000048160553},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.35920000076293945}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6341000199317932},{"id":"https://openalex.org/C1921717","wikidata":"https://www.wikidata.org/wiki/Q1334846","display_name":"Mahalanobis distance","level":2,"score":0.6101999878883362},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6093000173568726},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5078999996185303},{"id":"https://openalex.org/C72634772","wikidata":"https://www.wikidata.org/wiki/Q386824","display_name":"Data integration","level":2,"score":0.5},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.46050000190734863},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.4171000123023987},{"id":"https://openalex.org/C167981619","wikidata":"https://www.wikidata.org/wiki/Q1685498","display_name":"Cross entropy","level":3,"score":0.39070001244544983},{"id":"https://openalex.org/C2780898871","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Performance metric","level":2,"score":0.37720000743865967},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.3711000084877014},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.36550000309944153},{"id":"https://openalex.org/C115901376","wikidata":"https://www.wikidata.org/wiki/Q184199","display_name":"Automation","level":2,"score":0.3612000048160553},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35920000076293945},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3465999960899353},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.33469998836517334},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.33340001106262207},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.30959999561309814},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.3091000020503998},{"id":"https://openalex.org/C2983325608","wikidata":"https://www.wikidata.org/wiki/Q17084606","display_name":"Data association","level":3,"score":0.30480000376701355},{"id":"https://openalex.org/C19527686","wikidata":"https://www.wikidata.org/wiki/Q1665453","display_name":"System integration","level":2,"score":0.30219998955726624},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.29739999771118164},{"id":"https://openalex.org/C534262118","wikidata":"https://www.wikidata.org/wiki/Q177719","display_name":"Medical diagnosis","level":2,"score":0.29350000619888306},{"id":"https://openalex.org/C125014702","wikidata":"https://www.wikidata.org/wiki/Q4680749","display_name":"Adaptive learning","level":2,"score":0.28519999980926514},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.28279998898506165},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.26840001344680786},{"id":"https://openalex.org/C197298091","wikidata":"https://www.wikidata.org/wiki/Q5318963","display_name":"Dynamic data","level":2,"score":0.2639000117778778},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.25540000200271606}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.09009","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.09009","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.2604.09009","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.09009","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/16","score":0.42363762855529785,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Adaptive":[0],"medical":[1,142],"AI":[2],"models":[3],"often":[4],"face":[5],"performance":[6,29,95,114],"drops":[7],"in":[8,123,141],"dynamic":[9],"clinical":[10],"environments":[11],"due":[12],"to":[13,56,59,68],"data":[14,23,132],"drift.":[15],"We":[16],"propose":[17],"an":[18],"autonomous":[19],"continuous":[20],"monitoring":[21],"and":[22,50,77,134],"integration":[24],"framework":[25,112],"that":[26],"maintains":[27],"robust":[28],"over":[30],"time.":[31],"Focusing":[32],"on":[33,61,107],"glomerular":[34],"pathology":[35],"image":[36],"classification":[37],"(proliferative":[38],"vs.":[39],"non-proliferative":[40],"lupus":[41],"nephritis),":[42],"our":[43],"three-stage":[44],"method":[45],"uses":[46],"multi-metric":[47],"feature":[48],"analysis":[49],"Monte":[51],"Carlo":[52],"dropout-based":[53],"uncertainty":[54],"gating":[55],"decide":[57],"when":[58],"retrain":[60],"new":[62,116],"data.":[63],"Only":[64],"images":[65,92,117],"statistically":[66],"similar":[67],"the":[69,111],"training":[70],"distribution":[71],"(via":[72],"Euclidean,":[73],"cosine,":[74],"Mahalanobis":[75],"metrics)":[76],"with":[78,90,103],"low":[79],"predictive":[80],"entropy":[81],"are":[82],"integrated.":[83],"The":[84],"model":[85],"is":[86],"then":[87],"incrementally":[88],"retrained":[89],"these":[91],"under":[93],"strict":[94],"safeguards":[96],"(no":[97],"metric":[98],"degradation":[99],">5%).":[100],"In":[101],"experiments":[102],"a":[104,108],"ResNet18":[105],"ensemble":[106],"multi-center":[109],"dataset,":[110],"prevents":[113],"degradation:":[115],"were":[118],"added":[119],"without":[120],"significant":[121],"change":[122],"AUC":[124],"(~0.92)":[125],"or":[126],"accuracy":[127],"(~89%).":[128],"This":[129],"approach":[130],"addresses":[131],"shift":[133],"avoids":[135],"catastrophic":[136],"forgetting,":[137],"enabling":[138],"sustained":[139],"learning":[140],"imaging":[143],"AI.":[144]},"counts_by_year":[],"updated_date":"2026-04-14T06:08:25.285971","created_date":"2026-04-14T00:00:00"}