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Review
. 2025 Jul 8;29(1):290.
doi: 10.1186/s13054-025-05532-2.

Implementing Artificial Intelligence in Critical Care Medicine: a consensus of 22

Maurizio Cecconi  1   2 , Massimiliano Greco  3   4 , Benjamin Shickel  5   6 , Derek C Angus  7 , Heatherlee Bailey  8 , Elena Bignami  9 , Thierry Calandra  10 , Leo Anthony Celi  11   12   13 , Sharon Einav  14 , Paul Elbers  15 , Ari Ercole  16 , Hernando Gómez  7 , Michelle Ng Gong  17 , Matthieu Komorowski  18 , Vincent Liu  19 , Soojin Park  20 , Aarti Sarwal  21 , Christopher W Seymour  7 , Fernando G Zampieri  22 , Fabio Silvio Taccone  23 , Jean-Louis Vincent  23 , Azra Bihorac  5   6
Affiliations
Review

Implementing Artificial Intelligence in Critical Care Medicine: a consensus of 22

Maurizio Cecconi et al. Crit Care. .

Abstract

Artificial Intelligence (AI) is rapidly transforming the landscape of critical care, offering opportunities for enhanced diagnostic precision and personalized patient management. However, its integration into ICU clinical practice presents significant challenges related to equity, transparency, and the patient-clinician relationship. To address these concerns, a multidisciplinary team of experts was established to assess the current state and future trajectory of AI in critical care. This consensus identified key challenges and proposed actionable recommendations to guide AI implementation in this high-stakes field. Here we present a call to action for the critical care community, to bridge the gap between AI advancements and the need for humanized, patient-centred care. Our goal is to ensure a smooth transition to personalized medicine while, (1) maintaining equitable and unbiased decision-making, (2) fostering the development of a collaborative research network across ICUs, emergency departments, and operating rooms to promote data sharing and harmonization, and (3) addressing the necessary educational and regulatory shifts required for responsible AI deployment. AI integration into critical care demands coordinated efforts among clinicians, patients, industry leaders, and regulators to ensure patient safety and maximize societal benefit. The recommendations outlined here provide a foundation for the ethical and effective implementation of AI in critical care medicine.

Keywords: Artificial intelligence; Critical care medicine; Ethics; Healthcare innovation; Personalized medicine.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: Not applicable. Consent for publication: All authors consent to this publication. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Taxonomy of AI in critical care
Fig. 2
Fig. 2
Recommendations, according to development of standards for networking, data sharing and research, ethical challenges, regulations and societal challenges, and clinical practice
Fig. 3
Fig. 3
Summary of five representative AI use cases in critical care—ranging from waveform analysis to personalized clinician training—mapped across these 4 domains
See this image and copyright information in PMC

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