Use of artificial intelligence in critical care: opportunities and obstacles
Perhaps nowhere else in the healthcare system than in the intensive care unit environment are the challenges to create useful models with direct time-critical clinical applications more relevant and the obstacles to achieving those goals more massive. Machine learning-based artificial intelligence (...
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creator | Pinsky, Michael R Bedoya, Armando Bihorac, Azra Celi, Leo Churpek, Matthew Economou-Zavlanos, Nicoleta J Elbers, Paul Saria, Suchi Liu, Vincent Lyons, Patrick G Shickel, Benjamin Toral, Patrick Tscholl, David Clermont, Gilles |
description | Perhaps nowhere else in the healthcare system than in the intensive care unit environment are the challenges to create useful models with direct time-critical clinical applications more relevant and the obstacles to achieving those goals more massive. Machine learning-based artificial intelligence (AI) techniques to define states and predict future events are commonplace activities of modern life. However, their penetration into acute care medicine has been slow, stuttering and uneven. Major obstacles to widespread effective application of AI approaches to the real-time care of the critically ill patient exist and need to be addressed.
Clinical decision support systems (CDSSs) in acute and critical care environments support clinicians, not replace them at the bedside. As will be discussed in this review, the reasons are many and include the immaturity of AI-based systems to have situational awareness, the fundamental bias in many large databases that do not reflect the target population of patient being treated making fairness an important issue to address and technical barriers to the timely access to valid data and its display in a fashion useful for clinical workflow. The inherent "black-box" nature of many predictive algorithms and CDSS makes trustworthiness and acceptance by the medical community difficult. Logistically, collating and curating in real-time multidimensional data streams of various sources needed to inform the algorithms and ultimately display relevant clinical decisions support format that adapt to individual patient responses and signatures represent the efferent limb of these systems and is often ignored during initial validation efforts. Similarly, legal and commercial barriers to the access to many existing clinical databases limit studies to address fairness and generalizability of predictive models and management tools.
AI-based CDSS are evolving and are here to stay. It is our obligation to be good shepherds of their use and further development. |
doi_str_mv | 10.1186/s13054-024-04860-z |
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Clinical decision support systems (CDSSs) in acute and critical care environments support clinicians, not replace them at the bedside. As will be discussed in this review, the reasons are many and include the immaturity of AI-based systems to have situational awareness, the fundamental bias in many large databases that do not reflect the target population of patient being treated making fairness an important issue to address and technical barriers to the timely access to valid data and its display in a fashion useful for clinical workflow. The inherent "black-box" nature of many predictive algorithms and CDSS makes trustworthiness and acceptance by the medical community difficult. Logistically, collating and curating in real-time multidimensional data streams of various sources needed to inform the algorithms and ultimately display relevant clinical decisions support format that adapt to individual patient responses and signatures represent the efferent limb of these systems and is often ignored during initial validation efforts. Similarly, legal and commercial barriers to the access to many existing clinical databases limit studies to address fairness and generalizability of predictive models and management tools.
AI-based CDSS are evolving and are here to stay. It is our obligation to be good shepherds of their use and further development.</description><identifier>ISSN: 1364-8535</identifier><identifier>EISSN: 1466-609X</identifier><identifier>EISSN: 1364-8535</identifier><identifier>EISSN: 1366-609X</identifier><identifier>DOI: 10.1186/s13054-024-04860-z</identifier><identifier>PMID: 38589940</identifier><language>eng</language><publisher>England: BioMed Central Ltd</publisher><subject>Accountability ; Algorithms ; Artificial Intelligence ; Clinical decision making ; Collaboration ; Critical Care ; Critical care medicine ; Datasets ; Decision support systems ; Delivery of Health Care ; Electronic health records ; Forecasts and trends ; Health aspects ; Health care policy ; Humans ; Intensive care ; Intensive Care Units ; Machine learning ; Patients ; Privacy ; Technology application</subject><ispartof>Critical care (London, England), 2024-04, Vol.28 (1), p.113-113, Article 113</ispartof><rights>2024. The Author(s).</rights><rights>COPYRIGHT 2024 BioMed Central Ltd.</rights><rights>2024. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>The Author(s) 2024</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c449t-9b979368cda50cf98dc7233abd3ae473ca6b15d342e1911002313faa96c9dc903</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC11000355/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC11000355/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,881,27901,27902,53766,53768</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38589940$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Pinsky, Michael R</creatorcontrib><creatorcontrib>Bedoya, Armando</creatorcontrib><creatorcontrib>Bihorac, Azra</creatorcontrib><creatorcontrib>Celi, Leo</creatorcontrib><creatorcontrib>Churpek, Matthew</creatorcontrib><creatorcontrib>Economou-Zavlanos, Nicoleta J</creatorcontrib><creatorcontrib>Elbers, Paul</creatorcontrib><creatorcontrib>Saria, Suchi</creatorcontrib><creatorcontrib>Liu, Vincent</creatorcontrib><creatorcontrib>Lyons, Patrick G</creatorcontrib><creatorcontrib>Shickel, Benjamin</creatorcontrib><creatorcontrib>Toral, Patrick</creatorcontrib><creatorcontrib>Tscholl, David</creatorcontrib><creatorcontrib>Clermont, Gilles</creatorcontrib><title>Use of artificial intelligence in critical care: opportunities and obstacles</title><title>Critical care (London, England)</title><addtitle>Crit Care</addtitle><description>Perhaps nowhere else in the healthcare system than in the intensive care unit environment are the challenges to create useful models with direct time-critical clinical applications more relevant and the obstacles to achieving those goals more massive. Machine learning-based artificial intelligence (AI) techniques to define states and predict future events are commonplace activities of modern life. However, their penetration into acute care medicine has been slow, stuttering and uneven. Major obstacles to widespread effective application of AI approaches to the real-time care of the critically ill patient exist and need to be addressed.
Clinical decision support systems (CDSSs) in acute and critical care environments support clinicians, not replace them at the bedside. As will be discussed in this review, the reasons are many and include the immaturity of AI-based systems to have situational awareness, the fundamental bias in many large databases that do not reflect the target population of patient being treated making fairness an important issue to address and technical barriers to the timely access to valid data and its display in a fashion useful for clinical workflow. The inherent "black-box" nature of many predictive algorithms and CDSS makes trustworthiness and acceptance by the medical community difficult. Logistically, collating and curating in real-time multidimensional data streams of various sources needed to inform the algorithms and ultimately display relevant clinical decisions support format that adapt to individual patient responses and signatures represent the efferent limb of these systems and is often ignored during initial validation efforts. Similarly, legal and commercial barriers to the access to many existing clinical databases limit studies to address fairness and generalizability of predictive models and management tools.
AI-based CDSS are evolving and are here to stay. It is our obligation to be good shepherds of their use and further development.</description><subject>Accountability</subject><subject>Algorithms</subject><subject>Artificial Intelligence</subject><subject>Clinical decision making</subject><subject>Collaboration</subject><subject>Critical Care</subject><subject>Critical care medicine</subject><subject>Datasets</subject><subject>Decision support systems</subject><subject>Delivery of Health Care</subject><subject>Electronic health records</subject><subject>Forecasts and trends</subject><subject>Health aspects</subject><subject>Health care policy</subject><subject>Humans</subject><subject>Intensive care</subject><subject>Intensive Care Units</subject><subject>Machine learning</subject><subject>Patients</subject><subject>Privacy</subject><subject>Technology application</subject><issn>1364-8535</issn><issn>1466-609X</issn><issn>1364-8535</issn><issn>1366-609X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><recordid>eNptUk1rFTEUHUSxtfoHXMiAGzdTk8nHJG6klFYLD9xYcBcyd-48U-Ylz2SmYH997-ur1RYJIcnNOSecm1NVbzk75tzoj4ULpmTDWprSaNbcPKsOudS60cz-eE57oWVjlFAH1atSrhjjndHiZXUgjDLWSnZYrS4L1mmsfZ7DGCD4qQ5xxmkKa4yAdKghhzkAXYDP-KlO223K8xKpiKX2cahTX2YPE5bX1YvRTwXf3K9H1eX52ffTr83q25eL05NVA1LaubG97azQBgavGIzWDNC1Qvh-EB5lJ8DrnqtByBa55ZyxVnAxem812AEsE0fV573uduk3OADGOfvJbXPY-PzbJR_c45sYfrp1unY7MSaUIoUP9wo5_VqwzG4TCpBtHzEtxQlCsa4znSTo-yfQq7TkSP52KGqoVUb8Ra39hC7EMdHDsBN1J52xslVkh1DH_0HRGHATIEUcA9UfEdo9AXIqJeP4YJIztwuB24fAUQjcXQjcDZHe_dueB8qfXxe3ZSGsoA</recordid><startdate>20240408</startdate><enddate>20240408</enddate><creator>Pinsky, Michael R</creator><creator>Bedoya, Armando</creator><creator>Bihorac, Azra</creator><creator>Celi, Leo</creator><creator>Churpek, Matthew</creator><creator>Economou-Zavlanos, Nicoleta J</creator><creator>Elbers, Paul</creator><creator>Saria, Suchi</creator><creator>Liu, Vincent</creator><creator>Lyons, Patrick G</creator><creator>Shickel, Benjamin</creator><creator>Toral, Patrick</creator><creator>Tscholl, David</creator><creator>Clermont, Gilles</creator><general>BioMed Central Ltd</general><general>BioMed Central</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>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>M0S</scope><scope>M1P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20240408</creationdate><title>Use of artificial intelligence in critical care: opportunities and obstacles</title><author>Pinsky, Michael R ; Bedoya, Armando ; Bihorac, Azra ; Celi, Leo ; Churpek, Matthew ; Economou-Zavlanos, Nicoleta J ; Elbers, Paul ; Saria, Suchi ; Liu, Vincent ; Lyons, Patrick G ; Shickel, Benjamin ; Toral, Patrick ; Tscholl, David ; Clermont, Gilles</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c449t-9b979368cda50cf98dc7233abd3ae473ca6b15d342e1911002313faa96c9dc903</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Accountability</topic><topic>Algorithms</topic><topic>Artificial Intelligence</topic><topic>Clinical decision making</topic><topic>Collaboration</topic><topic>Critical Care</topic><topic>Critical care medicine</topic><topic>Datasets</topic><topic>Decision support systems</topic><topic>Delivery of Health Care</topic><topic>Electronic health records</topic><topic>Forecasts and trends</topic><topic>Health aspects</topic><topic>Health care policy</topic><topic>Humans</topic><topic>Intensive care</topic><topic>Intensive Care Units</topic><topic>Machine learning</topic><topic>Patients</topic><topic>Privacy</topic><topic>Technology application</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Pinsky, Michael R</creatorcontrib><creatorcontrib>Bedoya, Armando</creatorcontrib><creatorcontrib>Bihorac, Azra</creatorcontrib><creatorcontrib>Celi, Leo</creatorcontrib><creatorcontrib>Churpek, Matthew</creatorcontrib><creatorcontrib>Economou-Zavlanos, Nicoleta J</creatorcontrib><creatorcontrib>Elbers, Paul</creatorcontrib><creatorcontrib>Saria, Suchi</creatorcontrib><creatorcontrib>Liu, Vincent</creatorcontrib><creatorcontrib>Lyons, Patrick G</creatorcontrib><creatorcontrib>Shickel, Benjamin</creatorcontrib><creatorcontrib>Toral, Patrick</creatorcontrib><creatorcontrib>Tscholl, David</creatorcontrib><creatorcontrib>Clermont, Gilles</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Critical care (London, England)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Pinsky, Michael R</au><au>Bedoya, Armando</au><au>Bihorac, Azra</au><au>Celi, Leo</au><au>Churpek, Matthew</au><au>Economou-Zavlanos, Nicoleta J</au><au>Elbers, Paul</au><au>Saria, Suchi</au><au>Liu, Vincent</au><au>Lyons, Patrick G</au><au>Shickel, Benjamin</au><au>Toral, Patrick</au><au>Tscholl, David</au><au>Clermont, Gilles</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Use of artificial intelligence in critical care: opportunities and obstacles</atitle><jtitle>Critical care (London, England)</jtitle><addtitle>Crit Care</addtitle><date>2024-04-08</date><risdate>2024</risdate><volume>28</volume><issue>1</issue><spage>113</spage><epage>113</epage><pages>113-113</pages><artnum>113</artnum><issn>1364-8535</issn><eissn>1466-609X</eissn><eissn>1364-8535</eissn><eissn>1366-609X</eissn><abstract>Perhaps nowhere else in the healthcare system than in the intensive care unit environment are the challenges to create useful models with direct time-critical clinical applications more relevant and the obstacles to achieving those goals more massive. Machine learning-based artificial intelligence (AI) techniques to define states and predict future events are commonplace activities of modern life. However, their penetration into acute care medicine has been slow, stuttering and uneven. Major obstacles to widespread effective application of AI approaches to the real-time care of the critically ill patient exist and need to be addressed.
Clinical decision support systems (CDSSs) in acute and critical care environments support clinicians, not replace them at the bedside. As will be discussed in this review, the reasons are many and include the immaturity of AI-based systems to have situational awareness, the fundamental bias in many large databases that do not reflect the target population of patient being treated making fairness an important issue to address and technical barriers to the timely access to valid data and its display in a fashion useful for clinical workflow. The inherent "black-box" nature of many predictive algorithms and CDSS makes trustworthiness and acceptance by the medical community difficult. Logistically, collating and curating in real-time multidimensional data streams of various sources needed to inform the algorithms and ultimately display relevant clinical decisions support format that adapt to individual patient responses and signatures represent the efferent limb of these systems and is often ignored during initial validation efforts. Similarly, legal and commercial barriers to the access to many existing clinical databases limit studies to address fairness and generalizability of predictive models and management tools.
AI-based CDSS are evolving and are here to stay. It is our obligation to be good shepherds of their use and further development.</abstract><cop>England</cop><pub>BioMed Central Ltd</pub><pmid>38589940</pmid><doi>10.1186/s13054-024-04860-z</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Accountability Algorithms Artificial Intelligence Clinical decision making Collaboration Critical Care Critical care medicine Datasets Decision support systems Delivery of Health Care Electronic health records Forecasts and trends Health aspects Health care policy Humans Intensive care Intensive Care Units Machine learning Patients Privacy Technology application |
title | Use of artificial intelligence in critical care: opportunities and obstacles |
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