Machine Learning and Artificial Intelligence in Intensive Care Medicine - Critical Recalibrations from Rule-Based Systems to Frontier Models

Aug 4, 2026

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Artificial intelligence (AI) and machine learning (ML) are rapidly transforming clinical decision support systems (CDSSs) in intensive care units (ICUs), where real-time data volumes create both opportunity and pressure for timely decisions. This review traces machine intelligence evolution across ICU domains including early warning, sepsis management, mechanical ventilation, and diagnostic support. It emphasizes the shift from rule-based systems to data-driven ML and emerging frontier models while addressing constraints in transparency, workflow integration, bias, and regulatory requirements. Safe, effective, and ethical use depends on validated, human-centered systems with interdisciplinary support, technological literacy, prospective evaluation, and continuous monitoring.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages24

Reading Time1 h 0 min

AccessFree to download

Introduction

ICU as an AI testing ground

Clinical decision support systems and the shift to ML

Barriers and evidence needs

Review Scope and Focus

From rule-based systems to advanced machine intelligence

Domain applications in critical care

Review Machine Learning and Artificial Intelligence in Intensive Care Medicine: Critical Recalibrations from Rule-Based Systems to Frontier Models Pierre Hadweh 1, Alexandre Niset 1,2, Michele Salvagno 3, Mejdeddine Al Barajraji 1,4, Salim El Hadwe 1,5,6, Fabio Silvio Taccone 3 and Sami Barrit 1,7, * Academic Editor: Timothy E. Albertson Received: 28 April 2025 Revised: 31 May 2025 Accepted: 4 June 2025 Published: 6 June 2025 Citation: Hadweh, P.; Niset, A.; Salvagno, M.; Al Barajraji, M.; El Hadwe, S.; Taccone, F.S.; Barrit, S. Machine Learning and Artificial Intelligence in Intensive Care Medicine: Critical Recalibrations from Rule-Based Systems to Frontier Models. J. Clin. Med. 2025, 14, 4026 . [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)jcm14124026 Copyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)) . 1 Sciense, New York, NY 10027, USA; [pierrehadweh@gmail.com](pierrehadweh@gmail.com) (P.H.); [mejdi.albarajraji@gmail.com](mejdi.albarajraji@gmail.com) (M.A.B.); [salimhadweh15@gmail.com](salimhadweh15@gmail.com) (S.E.H.) 2 Pediatric Intensive Care Unit, Cliniques Universitaires Saint-Luc, 1200 Bruxelles, Belgium 3 Department of Intensive Care, Hôpital Universitaire de Bruxelles (HUB), Université Libre de Bruxelles (ULB), Anderlecht, 1070 Brussels, Belgium; [michele.salvagno1@gmail.com](michele.salvagno1@gmail.com) (M.S.); [fabio.taccone@ulb.be](fabio.taccone@ulb.be) (F.S.T.) 4 Department of Neurosurgery, CHR Citadelle, 4000 Liège, Belgium 5 Department of Clinical Neurosciences, University of Cambridge, Cambridge CB2 1TN, UK 6 Bioelectronics Laboratory, Department of Electrical Engineering, University of Cambridge, Cambridge CB2 1TN, UK 7 Institut Neuroscience des Systèmes (INS), Aix-Marseille Université, 13005 Marseille, France * Correspondence: [samibarrit@gmail.com](samibarrit@gmail.com) Abstract: Artificial intelligence (AI) and machine learning (ML) are rapidly transforming clinical decision support systems (CDSSs) in intensive care units (ICUs), where vast amounts of real-time data present both an opportunity and a challenge for timely clinical decision-making. Here, we trace the evolution of machine intelligence in critical care. This technology has been applied across key ICU domains such as early warning systems, sepsis management, mechanical ventilation, and diagnostic support. We highlight a transition from rule-based systems to more sophisticated machine learning approaches, including emerging frontier models. While these tools demonstrate strong potential to improve predictive performance and workflow efficiency, their implementation remains constrained by concerns around transparency, workflow integration, bias, and regulatory challenges. Ensuring the safe, effective, and ethical use of AI in intensive care will depend on validated, human-centered systems supported by transdisciplinary collaboration, technological literacy, prospective evaluation, and continuous monitoring. Keywords: intensive care medicine; critical care; clinical decision support systems; artificial intelligence; machine learning; sepsis; predictive analytics; deep learning; natural language processing; safety; privacy; large language models 1. Introduction The intensive care unit (ICU) represents both an ideal testing ground and a significant challenge for artificial intelligence (AI) applications in medicine. ICUs generate vast quantities of high-dimensional data, from continuous physiological monitoring, frequent laboratory testing, imaging studies, and detailed clinical documentation, which creates both an opportunity and a necessity for advanced computational approaches to data analy

FAQs

How does machine learning change clinical decision support in ICUs compared with rule-based systems?

ML enables data-driven detection of complex patterns and subtle signals, moving beyond rule-based logic tied to guidelines or expert consensus. This supports more adaptive risk assessment in dynamic critical illness contexts.

Which ICU domains are covered when reviewing AI and ML applications?

The review highlights applications in early warning systems, sepsis management, mechanical ventilation, and diagnostic support, focusing on how machine intelligence is applied across these domains.

What main concerns limit the implementation of AI tools in intensive care practice?

Key constraints include insufficient model transparency, unclear regulatory pathways, limited clinician trust, and risks related to workflow integration, bias, and safety. These barriers motivate stronger clinical evidence and rigorous validation.

Aug 4, 2026

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