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

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.

Keywords: Artificial intelligence; Clinical trials; Intensive care unit; Machine learning.

Fig. 1

PRISMA 2009 flow diagram of the study review process and the exclusion of studies; from [19]

Fig. 2

Proportion (%) of studies according to their design and the number of patients analyzed. *Studies with a retrospective design were stratified according to their level of validation (e.g. internal, external and no reported validation)

Fig. 3

Number of studies published according to their level of readiness and year of publication. The total number of studies reporting on model development and prototyping (level 3 and 4), increased rapidly from 30 studies per year in 2017 to 92 studies per year in 2019. Furthermore, the number of studies per year reporting on external validation (level 5) increased from two in 2017 to seven in 2019. The current movement is mainly horizontal whereas the desired movement is diagonal, i.e. towards clinical evaluation

Fig. 4

Percentage risk of bias according to the domain of assessment for all studies with a retrospective study design; assessed using PROBAST [23]