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AI-Driven Emergency Patient Flow Optimization is Both an Unmissable Opportunity and a Risk of Systematizing Health Disparities

dc.rights.licenseopenen_US
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorRUSSON, Dylan
hal.structure.identifierStatistics In System biology and Translational Medicine [SISTM]
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorAVALOS, Marta
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorGUERRA-ADAMES, Ariel
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorGIL-JARDINE, Cedric
ORCID: 0000-0001-5329-6405
IDREF: 159039223
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorLAGARDE, Emmanuel
dc.contributor.editorSoon Ae Chun
dc.contributor.editorTALBERT, Doug
dc.date.accessioned2024-09-18T11:38:11Z
dc.date.available2024-09-18T11:38:11Z
dc.date.issued2024-05-01
dc.date.conference2024-05-19
dc.identifier.issn2334-0754en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/201655
dc.description.abstractEnThere is a burgeoning interest in harnessing artificial intelligence (AI) to enhance patient flow within emergency departments (EDs). However, this advancement is accompanied by a significant risk: by relying on historical healthcare data, these AI tools may perpetuate existing systemic biases associated with gender, age, ethnicity, and socioeconomic status. This paper surveys studies identifying biases in ED data, offering context for concern about these biases. These insights are valuable for researchers developing AI to optimize ED workflows while accounting for ethical considerations.
dc.language.isoENen_US
dc.publisherLibraryPress@UFen_US
dc.publisherFLVC Library Servicesen_US
dc.rights.urihttp://creativecommons.org/licenses/by/
dc.subject.enHuman/AI bias
dc.subject.enSystematic discrimination issues via AI
dc.subject.enResponsible AI
dc.subject.enData diversity and representation
dc.subject.enLiterature survey
dc.subject.enAI in healthcare
dc.title.enAI-Driven Emergency Patient Flow Optimization is Both an Unmissable Opportunity and a Risk of Systematizing Health Disparities
dc.typeCommunication dans un congrèsen_US
dc.identifier.doi10.32473/flairs.37en_US
dc.subject.halSciences du Vivant [q-bio]/Santé publique et épidémiologieen_US
dc.subject.halInformatique [cs]/Intelligence artificielle [cs.AI]en_US
dc.subject.halMathématiques [math]/Optimisation et contrôle [math.OC]en_US
bordeaux.page4en_US
bordeaux.volume37en_US
bordeaux.hal.laboratoriesBordeaux Population Health Research Center (BPH) - UMR 1219en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.institutionINSERMen_US
bordeaux.institutionINRIAen_US
bordeaux.conference.titleFLAIRS-37 2024 - 37th International FLAIRS Conferenceen_US
bordeaux.countryusen_US
bordeaux.title.proceedingProceedings of FLAIRS-37, May 19-21, Sandestin Beach, FLen_US
bordeaux.teamSISTM_BPHen_US
bordeaux.teamAHEAD_BPHen_US
bordeaux.conference.cityMiramar Beachen_US
bordeaux.import.sourcehal
hal.identifierhal-04575571
hal.version1
hal.invitednonen_US
hal.proceedingsouien_US
hal.conference.organizerThe Florida Artificial Intelligence Research Societyen_US
hal.conference.end2024-05-21
hal.popularnonen_US
hal.audienceInternationaleen_US
hal.exportfalse
workflow.import.sourcehal
dc.rights.ccPas de Licence CCen_US
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.date=2024-05-01&rft.volume=37&rft.spage=4&rft.epage=4&rft.eissn=2334-0754&rft.issn=2334-0754&rft.au=RUSSON,%20Dylan&AVALOS,%20Marta&GUERRA-ADAMES,%20Ariel&GIL-JARDINE,%20Cedric&LAGARDE,%20Emmanuel&rft.genre=unknown


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