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Detecting Human Bias in Emergency Triage Using LLMs: Literature Review, Preliminary Study, and Experimental Plan

dc.rights.licenseopenen_US
hal.structure.identifierStatistics In System biology and Translational Medicine [SISTM]
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorAVALOS FERNANDEZ, Marta
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorCOHEN, Dalia
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorRUSSON, Dylan
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorDAVIDS, Melissa
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorDOREMUS, Oceane
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorCHENAIS, Gabrielle
ORCID: 0000-0003-2006-6149
IDREF: 119582597
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorTELLIER, Eric
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.date.accessioned2024-10-09T13:18:15Z
dc.date.available2024-10-09T13:18:15Z
dc.date.issued2024-05-19
dc.date.conference2024-05-19
dc.identifier.issn2334-0762en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/202348
dc.description.abstractEnThe surge in AI-based research for emergency healthcare poses challenges such as data protection compliance and the risk of exacerbating health inequalities. Human biases in demographic data used to train AI systems may indeed be replicated. Yet, AI also offers achance for a paradigm shift, acting as a tool to counteract human biases. Our study focuses on emergency triage, rapidly categorizing patients by severity upon arrival. Objectives include conducting a literature review to identify potential human biases in triage and presenting a preliminary study. This involves a qualitative survey to complement the review on factors influencing triage scores. Moreover, we analyze triage data descriptively and pilot AI-driven triage using an LLM with data from the local hospital. Finally, assembling these pieces, we outline an experimental plan to assess AI’s effectiveness in detecting human biases in triage data.
dc.language.isoENen_US
dc.publisherLibraryPress@UFen_US
dc.title.enDetecting Human Bias in Emergency Triage Using LLMs: Literature Review, Preliminary Study, and Experimental Plan
dc.typeCommunication dans un congrèsen_US
dc.subject.halSciences du Vivant [q-bio]/Santé publique et épidémiologieen_US
bordeaux.page6en_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 2024 - 37th International Florida Artificial Intelligence Research Society Conferenceen_US
bordeaux.countryusen_US
bordeaux.title.proceedingProceedings of FLAIRS-37en_US
bordeaux.teamAHEAD_BPHen_US
bordeaux.teamSISTM_BPHen_US
bordeaux.conference.cityMiramar Beachen_US
hal.identifierhal-04575557
hal.proceedingsouien_US
hal.conference.organizerThe Florida Artificial Intelligence Research Society (FLAIRS)en_US
hal.conference.end2024-05-21
hal.popularnonen_US
hal.audienceInternationaleen_US
hal.exportfalse
dc.rights.ccPas de Licence CCen_US
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.date=2024-05-19&rft.volume=37&rft.spage=6&rft.epage=6&rft.eissn=2334-0762&rft.issn=2334-0762&rft.au=AVALOS%20FERNANDEZ,%20Marta&COHEN,%20Dalia&RUSSON,%20Dylan&DAVIDS,%20Melissa&DOREMUS,%20Oceane&rft.genre=unknown


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