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      Artificial Intelligence in Critical Care Medicine

      review-article
      Author(s): 1 , , 2 , 2
      Publication date (Electronic):
      Journal: Critical Care
      Publisher: BioMed Central

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          This article is one of ten reviews selected from the Annual Update in Intensive Care and Emergency Medicine 2022. Other selected articles can be found online at https://www.biomedcentral.com/collections/annualupdate2022. Further information about the Annual Update in Intensive Care and Emergency Medicine is available from https://link.springer.com/bookseries/8901.

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          Most cited references48

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          High-performance medicine: the convergence of human and artificial intelligence

          Eric Topol (2019)
          The use of artificial intelligence, and the deep-learning subtype in particular, has been enabled by the use of labeled big data, along with markedly enhanced computing power and cloud storage, across all sectors. In medicine, this is beginning to have an impact at three levels: for clinicians, predominantly via rapid, accurate image interpretation; for health systems, by improving workflow and the potential for reducing medical errors; and for patients, by enabling them to process their own data to promote health. The current limitations, including bias, privacy and security, and lack of transparency, along with the future directions of these applications will be discussed in this article. Over time, marked improvements in accuracy, productivity, and workflow will likely be actualized, but whether that will be used to improve the patient-doctor relationship or facilitate its erosion remains to be seen.
          Article 0 comments Cited 1274 times – based on 0 reviews     Review now
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            Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

            Cynthia Rudin (2019)
            Black box machine learning models are currently being used for high stakes decision-making throughout society, causing problems throughout healthcare, criminal justice, and in other domains. People have hoped that creating methods for explaining these black box models will alleviate some of these problems, but trying to explain black box models, rather than creating models that are interpretable in the first place, is likely to perpetuate bad practices and can potentially cause catastrophic harm to society. There is a way forward - it is to design models that are inherently interpretable. This manuscript clarifies the chasm between explaining black boxes and using inherently interpretable models, outlines several key reasons why explainable black boxes should be avoided in high-stakes decisions, identifies challenges to interpretable machine learning, and provides several example applications where interpretable models could potentially replace black box models in criminal justice, healthcare, and computer vision.
            Article 0 comments Cited 990 times – based on 0 reviews     Review now
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              Interleukin-6 Receptor Antagonists in Critically Ill Patients with Covid-19

              (2021)
              Abstract Background The efficacy of interleukin-6 receptor antagonists in critically ill patients with coronavirus disease 2019 (Covid-19) is unclear. Methods We evaluated tocilizumab and sarilumab in an ongoing international, multifactorial, adaptive platform trial. Adult patients with Covid-19, within 24 hours after starting organ support in the intensive care unit (ICU), were randomly assigned to receive tocilizumab (8 mg per kilogram of body weight), sarilumab (400 mg), or standard care (control). The primary outcome was respiratory and cardiovascular organ support–free days, on an ordinal scale combining in-hospital death (assigned a value of −1) and days free of organ support to day 21. The trial uses a Bayesian statistical model with predefined criteria for superiority, efficacy, equivalence, or futility. An odds ratio greater than 1 represented improved survival, more organ support–free days, or both. Results Both tocilizumab and sarilumab met the predefined criteria for efficacy. At that time, 353 patients had been assigned to tocilizumab, 48 to sarilumab, and 402 to control. The median number of organ support–free days was 10 (interquartile range, −1 to 16) in the tocilizumab group, 11 (interquartile range, 0 to 16) in the sarilumab group, and 0 (interquartile range, −1 to 15) in the control group. The median adjusted cumulative odds ratios were 1.64 (95% credible interval, 1.25 to 2.14) for tocilizumab and 1.76 (95% credible interval, 1.17 to 2.91) for sarilumab as compared with control, yielding posterior probabilities of superiority to control of more than 99.9% and of 99.5%, respectively. An analysis of 90-day survival showed improved survival in the pooled interleukin-6 receptor antagonist groups, yielding a hazard ratio for the comparison with the control group of 1.61 (95% credible interval, 1.25 to 2.08) and a posterior probability of superiority of more than 99.9%. All secondary analyses supported efficacy of these interleukin-6 receptor antagonists. Conclusions In critically ill patients with Covid-19 receiving organ support in ICUs, treatment with the interleukin-6 receptor antagonists tocilizumab and sarilumab improved outcomes, including survival. (REMAP-CAP ClinicalTrials.gov number, NCT02735707.)
              Article 0 comments Cited 856 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Joo Heung Yoon: yoonjh@upmc.edu
                Journal
                Journal ID (nlm-ta): Crit Care
                Title: Critical Care
                Publisher: BioMed Central (London )
                ISSN (Print): 1364-8535
                ISSN (Electronic): 1466-609X
                Publication date (Electronic): 22 March 2022
                Publication date Collection: 2022
                Volume: 26
                Electronic Location Identifier: 75
                Affiliations
                [1 ]GRID grid.21925.3d, ISNI 0000 0004 1936 9000, Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, , University of Pittsburgh, ; Pittsburgh, PA USA
                [2 ]GRID grid.21925.3d, ISNI 0000 0004 1936 9000, Department of Critical Care Medicine, , University of Pittsburgh, ; Pittsburgh, PA USA
                Article
                Publisher ID: 3915
                DOI: 10.1186/s13054-022-03915-3
                PMC ID: 8951650
                PubMed ID: 35337366
                SO-VID: 0ec49d0f-9ab1-43cf-984d-f4fde9fb945e
                Copyright © © Yoon et al. 2022
                History
                Categories
                Subject: Review
                Custom metadata
                issue-copyright-statement © The Author(s) 2022

                ScienceOpen disciplines: Emergency medicine & Trauma
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                ScienceOpen disciplines: Emergency medicine & Trauma

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