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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Veröffentlicht in:Critical care (London, England) England), 2024-04, Vol.28 (1), p.113-113, Article 113
Hauptverfasser: 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
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container_end_page 113
container_issue 1
container_start_page 113
container_title Critical care (London, England)
container_volume 28
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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source MEDLINE; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; PubMed Central; Springer Nature OA/Free Journals; SpringerLink Journals - AutoHoldings
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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