Health AI Central
Peer-Reviewed Publication
Artif Intell Med2026;181103515.November 1, 2026Journal Article

A perspective on foundation models in intensive care medicine.

Carl Harris1, Samuel Schmidgall2, Sampath Rapuri1, Kevin Hwang3, Michael Moor4, Robert D Stevens5
1Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
2Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA.
3Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
4Department of Biosystems Science and Engineering, ETH Zurich, Switzerland.
5Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA; Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA; Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University, Baltimore, MD, USA. Electronic address: rsteven1@jh.edu.
PubMed DOI

Abstract

Foundation models (FMs) pretrained with self-supervised objectives on heterogeneous ICU data have capabilities which supersede siloed, single-task predictive analytic paradigms. Here we present a framework for applying FMs in intensive care and a roadmap for safe, effective deployment. We argue that FMs applied in the time-critical, data-dense environment of intensive care could support rapid lear…

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