Bias at warp speed: how AI may contribute to the disparities gap in the time of COVID-19.
Identifieur interne : 000101 ( Main/Exploration ); précédent : 000100; suivant : 000102Bias at warp speed: how AI may contribute to the disparities gap in the time of COVID-19.
Auteurs : Eliane Röösli [Suisse, États-Unis] ; Brian Rice [États-Unis] ; Tina Hernandez-Boussard [États-Unis]Source :
- Journal of the American Medical Informatics Association : JAMIA [ 1527-974X ] ; 2021.
Abstract
The COVID-19 pandemic is presenting a disproportionate impact on minorities in terms of infection rate, hospitalizations, and mortality. Many believe artificial intelligence (AI) is a solution to guide clinical decision-making for this novel disease, resulting in the rapid dissemination of underdeveloped and potentially biased models, which may exacerbate the disparities gap. We believe there is an urgent need to enforce the systematic use of reporting standards and develop regulatory frameworks for a shared COVID-19 data source to address the challenges of bias in AI during this pandemic. There is hope that AI can help guide treatment decisions within this crisis; yet given the pervasiveness of biases, a failure to proactively develop comprehensive mitigation strategies during the COVID-19 pandemic risks exacerbating existing health disparities.
DOI: 10.1093/jamia/ocaa210
PubMed: 32805004
PubMed Central: PMC7454645
Affiliations:
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Röösli</name><affiliation wicri:level="3"><nlm:affiliation>School of Life Sciences, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland.</nlm:affiliation><country xml:lang="fr">Suisse</country><wicri:regionArea>School of Life Sciences, Swiss Federal Institute of Technology (EPFL), Lausanne</wicri:regionArea><placeName><settlement type="city">Lausanne</settlement><region nuts="3" type="region">Canton de Vaud</region></placeName></affiliation><affiliation wicri:level="4"><nlm:affiliation>Department of Medicine (Biomedical Informatics), Stanford University, Stanford, California, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Medicine (Biomedical Informatics), Stanford University, Stanford, California</wicri:regionArea><placeName><region type="state">Californie</region><settlement type="city">Stanford (Californie)</settlement></placeName><orgName type="university">Université Stanford</orgName></affiliation></author><author><name sortKey="Rice, Brian" sort="Rice, Brian" uniqKey="Rice B" first="Brian" last="Rice">Brian Rice</name><affiliation wicri:level="4"><nlm:affiliation>Department of Emergency Medicine, Stanford University, Stanford, California, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Emergency Medicine, Stanford University, Stanford, California</wicri:regionArea><placeName><region type="state">Californie</region><settlement type="city">Stanford (Californie)</settlement></placeName><orgName type="university">Université Stanford</orgName></affiliation></author><author><name sortKey="Hernandez Boussard, Tina" sort="Hernandez Boussard, Tina" uniqKey="Hernandez Boussard T" first="Tina" last="Hernandez-Boussard">Tina Hernandez-Boussard</name><affiliation wicri:level="4"><nlm:affiliation>Department of Medicine (Biomedical Informatics), Stanford University, Stanford, California, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Medicine (Biomedical Informatics), Stanford University, Stanford, California</wicri:regionArea><placeName><region type="state">Californie</region><settlement type="city">Stanford (Californie)</settlement></placeName><orgName type="university">Université Stanford</orgName></affiliation><affiliation wicri:level="4"><nlm:affiliation>Department of Biomedical Data Sciences, Stanford University, Stanford, California, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Biomedical Data Sciences, Stanford University, Stanford, California</wicri:regionArea><placeName><region type="state">Californie</region><settlement type="city">Stanford (Californie)</settlement></placeName><orgName type="university">Université Stanford</orgName></affiliation></author></analytic><series><title level="j">Journal of the American Medical Informatics Association : JAMIA</title><idno type="eISSN">1527-974X</idno><imprint><date when="2021" type="published">2021</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">The COVID-19 pandemic is presenting a disproportionate impact on minorities in terms of infection rate, hospitalizations, and mortality. Many believe artificial intelligence (AI) is a solution to guide clinical decision-making for this novel disease, resulting in the rapid dissemination of underdeveloped and potentially biased models, which may exacerbate the disparities gap. We believe there is an urgent need to enforce the systematic use of reporting standards and develop regulatory frameworks for a shared COVID-19 data source to address the challenges of bias in AI during this pandemic. There is hope that AI can help guide treatment decisions within this crisis; yet given the pervasiveness of biases, a failure to proactively develop comprehensive mitigation strategies during the COVID-19 pandemic risks exacerbating existing health disparities.</div></front></TEI><pubmed><MedlineCitation Status="In-Process" Owner="NLM"><PMID Version="1">32805004</PMID><DateRevised><Year>2021</Year><Month>01</Month><Day>22</Day></DateRevised><Article PubModel="Print"><Journal><ISSN IssnType="Electronic">1527-974X</ISSN><JournalIssue CitedMedium="Internet"><Volume>28</Volume><Issue>1</Issue><PubDate><Year>2021</Year><Month>01</Month><Day>15</Day></PubDate></JournalIssue><Title>Journal of the American Medical Informatics Association : JAMIA</Title><ISOAbbreviation>J Am Med Inform Assoc</ISOAbbreviation></Journal><ArticleTitle>Bias at warp speed: how AI may contribute to the disparities gap in the time of COVID-19.</ArticleTitle><Pagination><MedlinePgn>190-192</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1093/jamia/ocaa210</ELocationID><Abstract><AbstractText>The COVID-19 pandemic is presenting a disproportionate impact on minorities in terms of infection rate, hospitalizations, and mortality. Many believe artificial intelligence (AI) is a solution to guide clinical decision-making for this novel disease, resulting in the rapid dissemination of underdeveloped and potentially biased models, which may exacerbate the disparities gap. We believe there is an urgent need to enforce the systematic use of reporting standards and develop regulatory frameworks for a shared COVID-19 data source to address the challenges of bias in AI during this pandemic. There is hope that AI can help guide treatment decisions within this crisis; yet given the pervasiveness of biases, a failure to proactively develop comprehensive mitigation strategies during the COVID-19 pandemic risks exacerbating existing health disparities.</AbstractText><CopyrightInformation>© The Author(s) 2020. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Röösli</LastName><ForeName>Eliane</ForeName><Initials>E</Initials><AffiliationInfo><Affiliation>School of Life Sciences, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Department of Medicine (Biomedical Informatics), Stanford University, Stanford, California, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Rice</LastName><ForeName>Brian</ForeName><Initials>B</Initials><AffiliationInfo><Affiliation>Department of Emergency Medicine, Stanford University, Stanford, California, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Hernandez-Boussard</LastName><ForeName>Tina</ForeName><Initials>T</Initials><AffiliationInfo><Affiliation>Department of Medicine (Biomedical Informatics), Stanford University, Stanford, California, USA.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Department of Biomedical Data Sciences, Stanford University, Stanford, California, USA.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>J Am Med Inform Assoc</MedlineTA><NlmUniqueID>9430800</NlmUniqueID><ISSNLinking>1067-5027</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="Y">COVID</Keyword><Keyword MajorTopicYN="Y">artificial intelligence</Keyword><Keyword MajorTopicYN="Y">bias</Keyword><Keyword MajorTopicYN="Y">disparities, reporting standards </Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2020</Year><Month>08</Month><Day>11</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2020</Year><Month>08</Month><Day>13</Day></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>8</Month><Day>18</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>8</Month><Day>18</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>8</Month><Day>18</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">32805004</ArticleId><ArticleId IdType="pii">5893483</ArticleId><ArticleId IdType="doi">10.1093/jamia/ocaa210</ArticleId><ArticleId IdType="pmc">PMC7454645</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list><country><li>Suisse</li><li>États-Unis</li></country><region><li>Californie</li><li>Canton de Vaud</li></region><settlement><li>Lausanne</li><li>Stanford (Californie)</li></settlement><orgName><li>Université Stanford</li></orgName></list><tree><country name="Suisse"><region name="Canton de Vaud"><name sortKey="Roosli, Eliane" sort="Roosli, Eliane" uniqKey="Roosli E" first="Eliane" last="Röösli">Eliane Röösli</name></region></country><country name="États-Unis"><region name="Californie"><name sortKey="Roosli, Eliane" sort="Roosli, Eliane" uniqKey="Roosli E" first="Eliane" last="Röösli">Eliane Röösli</name></region><name sortKey="Hernandez Boussard, Tina" sort="Hernandez Boussard, Tina" uniqKey="Hernandez Boussard T" first="Tina" last="Hernandez-Boussard">Tina Hernandez-Boussard</name><name sortKey="Hernandez Boussard, Tina" sort="Hernandez Boussard, Tina" uniqKey="Hernandez Boussard T" first="Tina" last="Hernandez-Boussard">Tina Hernandez-Boussard</name><name sortKey="Rice, Brian" sort="Rice, Brian" uniqKey="Rice B" first="Brian" last="Rice">Brian Rice</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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