The future of artificial intelligence in intensive care

moving from predictive to actionable AI

Journal article (2023)

Authors

J.M. Smit Pattern Recognition and Bioinformatics - EEMCS , Erasmus MC
J.H. Krijthe Pattern Recognition and Bioinformatics - EEMCS
Jasper van Bommel Erasmus MC
D.A.M.P.J. Gommers Biomechanical Engineering
M.J.T. Reinders Pattern Recognition and Bioinformatics - EEMCS
Research Group
Pattern Recognition and Bioinformatics (EEMCS) (TU Delft)
Copyright: © 2023 , J.M. Smit, J.H. Krijthe, Jasper van Bommel, M. E. van Genderen, J. A. Labrecque, M. Komorowski, D.A.M.P.J. Gommers, M.J.T. Reinders
DOI
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https://doi.org/10.1007/s00134-023-07102-y
More Info
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Published Date

2023

Language

English

Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Intelligent Systems

Research Group

Pattern Recognition and Bioinformatics

Abstract

Artificial intelligence (AI) research in the intensive care unit (ICU) mainly focuses on developing models (from linear regression to deep learning) to predict out-
comes, such as mortality or sepsis [1, 2]. However, there is another important aspect of AI that is typically not framed as AI (although it may be more worthy of the name), which is the prediction of patient outcomes or events that would result from different actions, known as causal inference [3, 4]. This aspect of AI is crucial for decision-making in the ICU. To emphasize the impor- tance of causal inference, we propose to refer to any data- driven model used for causal inference tasks as ‘action- able AI’, as opposed to ‘predictive AI’, and discuss how these models could provide meaningful decision support in the ICU.