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      Moving from bytes to bedside: a systematic review on the use of artificial intelligence in the intensive care unit

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          Abstract

          Purpose

          Due to the increasing demand for intensive care unit (ICU) treatment, and to improve quality and efficiency of care, there is a need for adequate and efficient clinical decision-making. The advancement of artificial intelligence (AI) technologies has resulted in the development of prediction models, which might aid clinical decision-making. This systematic review seeks to give a contemporary overview of the current maturity of AI in the ICU, the research methods behind these studies, and the risk of bias in these studies.

          Methods

          A systematic search was conducted in Embase, Medline, Web of Science Core Collection and Cochrane Central Register of Controlled Trials databases to identify eligible studies. Studies using AI to analyze ICU data were considered eligible. Specifically, the study design, study aim, dataset size, level of validation, level of readiness, and the outcomes of clinical trials were extracted. Risk of bias in individual studies was evaluated by the Prediction model Risk Of Bias ASsessment Tool (PROBAST).

          Results

          Out of 6455 studies identified through literature search, 494 were included. The most common study design was retrospective [476 studies (96.4% of all studies)] followed by prospective observational [8 (1.6%)] and clinical [10 (2%)] trials. 378 (80.9%) retrospective studies were classified as high risk of bias. No studies were identified that reported on the outcome evaluation of an AI model integrated in routine clinical practice.

          Conclusion

          The vast majority of developed ICU-AI models remain within the testing and prototyping environment; only a handful were actually evaluated in clinical practice. A uniform and structured approach can support the development, safe delivery, and implementation of AI to determine clinical benefit in the ICU.

          Supplementary Information

          The online version contains supplementary material available at 10.1007/s00134-021-06446-7.

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          Most cited references50

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          Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement

          David Moher, Alessandro Liberati, Jennifer Tetzlaff … (2011)
          David Moher and colleagues introduce PRISMA, an update of the QUOROM guidelines for reporting systematic reviews and meta-analyses
          Article 0 comments Cited 4915 times – based on 0 reviews     Review now
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            Dermatologist-level classification of skin cancer with deep neural networks

            Andre Esteva, Brett Kuprel, Roberto Chacón-Novoa … (2017)
            Skin cancer, the most common human malignancy, is primarily diagnosed visually, beginning with an initial clinical screening and followed potentially by dermoscopic analysis, a biopsy and histopathological examination. Automated classification of skin lesions using images is a challenging task owing to the fine-grained variability in the appearance of skin lesions. Deep convolutional neural networks (CNNs) show potential for general and highly variable tasks across many fine-grained object categories. Here we demonstrate classification of skin lesions using a single CNN, trained end-to-end from images directly, using only pixels and disease labels as inputs. We train a CNN using a dataset of 129,450 clinical images—two orders of magnitude larger than previous datasets—consisting of 2,032 different diseases. We test its performance against 21 board-certified dermatologists on biopsy-proven clinical images with two critical binary classification use cases: keratinocyte carcinomas versus benign seborrheic keratoses; and malignant melanomas versus benign nevi. The first case represents the identification of the most common cancers, the second represents the identification of the deadliest skin cancer. The CNN achieves performance on par with all tested experts across both tasks, demonstrating an artificial intelligence capable of classifying skin cancer with a level of competence comparable to dermatologists. Outfitted with deep neural networks, mobile devices can potentially extend the reach of dermatologists outside of the clinic. It is projected that 6.3 billion smartphone subscriptions will exist by the year 2021 (ref. 13) and can therefore potentially provide low-cost universal access to vital diagnostic care.
            Article 0 comments Cited 2018 times – based on 0 reviews     Review now
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              SPIRIT 2013 statement: defining standard protocol items for clinical trials.

              An-Wen Chan, Jennifer M Tetzlaff, Douglas G. Altman … (2013)
              The protocol of a clinical trial serves as the foundation for study planning, conduct, reporting, and appraisal. However, trial protocols and existing protocol guidelines vary greatly in content and quality. This article describes the systematic development and scope of SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) 2013, a guideline for the minimum content of a clinical trial protocol.The 33-item SPIRIT checklist applies to protocols for all clinical trials and focuses on content rather than format. The checklist recommends a full description of what is planned; it does not prescribe how to design or conduct a trial. By providing guidance for key content, the SPIRIT recommendations aim to facilitate the drafting of high-quality protocols. Adherence to SPIRIT would also enhance the transparency and completeness of trial protocols for the benefit of investigators, trial participants, patients, sponsors, funders, research ethics committees or institutional review boards, peer reviewers, journals, trial registries, policymakers, regulators, and other key stakeholders.
              Article 0 comments Cited 1893 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Michel E. van Genderen: m.vangenderen@erasmusmc.nl
                Journal
                Journal ID (nlm-ta): Intensive Care Med
                Journal ID (iso-abbrev): Intensive Care Med
                Title: Intensive Care Medicine
                Publisher: Springer Berlin Heidelberg (Berlin/Heidelberg )
                ISSN (Print): 0342-4642
                ISSN (Electronic): 1432-1238
                Publication date (Electronic): 5 June 2021
                Publication date PMC-release: 5 June 2021
                Pages: 1-11
                Affiliations
                [1 ]GRID grid.5645.2, ISNI 000000040459992X, Department of Adult Intensive Care, , Erasmus MC University Medical Center, ; Room Ne-413, Doctor Molewaterplein 40, 3015 GD Rotterdam, The Netherlands
                [2 ]SAS Institute, Health Care Analytics, Huizen, The Netherlands
                Author information
                http://orcid.org/0000-0003-4484-0995
                Article
                Publisher ID: 6446
                DOI: 10.1007/s00134-021-06446-7
                PMC ID: 8178026
                PubMed ID: 34089064
                SO-VID: 97d1eb8d-1593-40e4-9fcf-bc069e848a2f
                Copyright © © The Author(s) 2021
                License:

                Open AccessThis article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which permits any non-commercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc/4.0/.

                History
                Date received : 24 March 2021
                Date accepted : 23 May 2021
                Categories
                Subject: Systematic Review

                ScienceOpen disciplines: Emergency medicine & Trauma
                Keywords: artificial intelligence,machine learning,intensive care unit,clinical trials
                Data availability:
                ScienceOpen disciplines: Emergency medicine & Trauma
                Keywords: artificial intelligence, machine learning, intensive care unit, clinical trials

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