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Minimizing bias when using artificial intelligence in critical care medicine
•As artificial intelligence (AI) use in critical care increases, identifying and minimizing sources of bias is essential•Bias can be introduced (and be minimized) at all stages of the AI lifecycle•Reliance on AI in clinical practice without addressing bias may result in unfair and inequitable treatm...
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Published in: | Journal of critical care 2024-08, Vol.82, p.154796, Article 154796 |
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container_start_page | 154796 |
container_title | Journal of critical care |
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creator | Ranard, Benjamin L. Park, Soojin Jia, Yugang Zhang, Yiye Alwan, Fatima Celi, Leo Anthony Lusczek, Elizabeth R. |
description | •As artificial intelligence (AI) use in critical care increases, identifying and minimizing sources of bias is essential•Bias can be introduced (and be minimized) at all stages of the AI lifecycle•Reliance on AI in clinical practice without addressing bias may result in unfair and inequitable treatment of patients•Diverse, multidisciplinary teams that are attuned to sources of bias may have the best chance of minimizing bias in AI |
doi_str_mv | 10.1016/j.jcrc.2024.154796 |
format | article |
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Artificial Intelligence ; Bias ; COVID-19 ; Critical Care ; Critical Care - methods ; Decision making ; Disparities ; Electronic health records ; Fairness ; Health care access ; Health care expenditures ; Health equity ; Humans ; Intensive care ; Machine learning ; Missing data ; Palliative care ; Pandemics ; Sepsis ; Ventilators</subject><ispartof>Journal of critical care, 2024-08, Vol.82, p.154796, Article 154796</ispartof><rights>2023</rights><rights>Copyright Elsevier Limited Aug 2024</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c384t-d364b22b02ee35b1c9927139cb21323d7f4e5204e83285945044561c076828cf3</citedby><cites>FETCH-LOGICAL-c384t-d364b22b02ee35b1c9927139cb21323d7f4e5204e83285945044561c076828cf3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,777,781,27905,27906</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38552451$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Ranard, Benjamin L.</creatorcontrib><creatorcontrib>Park, Soojin</creatorcontrib><creatorcontrib>Jia, Yugang</creatorcontrib><creatorcontrib>Zhang, Yiye</creatorcontrib><creatorcontrib>Alwan, Fatima</creatorcontrib><creatorcontrib>Celi, Leo Anthony</creatorcontrib><creatorcontrib>Lusczek, Elizabeth R.</creatorcontrib><title>Minimizing bias when using artificial intelligence in critical care medicine</title><title>Journal of critical care</title><addtitle>J Crit Care</addtitle><description>•As artificial intelligence (AI) use in critical care increases, identifying and minimizing sources of bias is essential•Bias can be introduced (and be minimized) at all stages of the AI lifecycle•Reliance on AI in clinical practice without addressing bias may result in unfair and inequitable treatment of patients•Diverse, multidisciplinary teams that are attuned to sources of bias may have the best chance of minimizing bias in AI</description><subject>Algorithms</subject><subject>Artificial Intelligence</subject><subject>Bias</subject><subject>COVID-19</subject><subject>Critical Care</subject><subject>Critical Care - methods</subject><subject>Decision making</subject><subject>Disparities</subject><subject>Electronic health records</subject><subject>Fairness</subject><subject>Health care access</subject><subject>Health care expenditures</subject><subject>Health equity</subject><subject>Humans</subject><subject>Intensive care</subject><subject>Machine learning</subject><subject>Missing data</subject><subject>Palliative care</subject><subject>Pandemics</subject><subject>Sepsis</subject><subject>Ventilators</subject><issn>0883-9441</issn><issn>1557-8615</issn><issn>1557-8615</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNp9kE1Lw0AQhhdRbK3-AQ8S8OIldT-TDXiR4hdUvOh5STaTOqFN6m6i6K93Q6oHD56GYZ73ZXgIOWV0zihLLut5bZ2dc8rlnCmZZskemTKl0lgnTO2TKdVaxJmUbEKOvK8pZakQ6pBMhFaKS8WmZPmIDW7wC5tVVGDuo49XaKLeD3vuOqzQYr6OsOlgvcYVNBbCElmHHdpwsLmDaANlwBo4JgdVvvZwspsz8nJ787y4j5dPdw-L62VshZZdXIpEFpwXlAMIVTCbZTxlIrMFZ4KLMq0kKE4laMG1yqSiUqqEWZommmtbiRm5GHu3rn3rwXdmg96GB_MG2t4bQTlXSZbqNKDnf9C67V0TvgtUwphQUvNA8ZGyrvXeQWW2Dje5-zSMmsG1qc3g2gyuzeg6hM521X0RDPxGfuQG4GoEILh4R3DGWxwMlujAdqZs8b_-b1R0jWg</recordid><startdate>202408</startdate><enddate>202408</enddate><creator>Ranard, Benjamin L.</creator><creator>Park, Soojin</creator><creator>Jia, Yugang</creator><creator>Zhang, Yiye</creator><creator>Alwan, Fatima</creator><creator>Celi, Leo Anthony</creator><creator>Lusczek, Elizabeth R.</creator><general>Elsevier Inc</general><general>Elsevier Limited</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7RV</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AN0</scope><scope>ASE</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FPQ</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>K6X</scope><scope>K9.</scope><scope>KB0</scope><scope>M0S</scope><scope>M1P</scope><scope>M2O</scope><scope>MBDVC</scope><scope>NAPCQ</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>7X8</scope></search><sort><creationdate>202408</creationdate><title>Minimizing bias when using artificial intelligence in critical care medicine</title><author>Ranard, Benjamin L. ; 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source | ScienceDirect Freedom Collection 2022-2024 |
subjects | Algorithms Artificial Intelligence Bias COVID-19 Critical Care Critical Care - methods Decision making Disparities Electronic health records Fairness Health care access Health care expenditures Health equity Humans Intensive care Machine learning Missing data Palliative care Pandemics Sepsis Ventilators |
title | Minimizing bias when using artificial intelligence in critical care medicine |
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