Show simple item record

dc.contributor.authorCecconi, Maurizio
dc.contributor.authorGreco, Massimiliano
dc.contributor.authorShickel, Benjamin
dc.contributor.authorAngus, Derek C.
dc.contributor.authorBailey, Heatherlee
dc.contributor.authorBignami, Elena
dc.contributor.authorCalandra, Thierry
dc.contributor.authorCeli, Leo A.
dc.contributor.authorEinav, Sharon
dc.contributor.authorElbers, Paul
dc.contributor.authorErcole, Ari
dc.contributor.authorGómez, Hernando
dc.contributor.authorGong, Michelle N.
dc.contributor.authorKomorowski, Matthieu
dc.contributor.authorLiu, Vincent
dc.date.accessioned2025-08-25T15:59:56Z
dc.date.available2025-08-25T15:59:56Z
dc.date.issued2025-07-08
dc.identifier.urihttps://hdl.handle.net/1721.1/162474
dc.description.abstractArtificial 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.en_US
dc.publisherBioMed Centralen_US
dc.relation.isversionofhttps://doi.org/10.1186/s13054-025-05532-2en_US
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivativesen_US
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.sourceBioMed Centralen_US
dc.titleImplementing Artificial Intelligence in Critical Care Medicine: a consensus of 22en_US
dc.typeArticleen_US
dc.identifier.citationCecconi, M., Greco, M., Shickel, B. et al. Implementing Artificial Intelligence in Critical Care Medicine: a consensus of 22. Crit Care 29, 290 (2025).en_US
dc.contributor.departmentHarvard--MIT Program in Health Sciences and Technology. Laboratory for Computational Physiologyen_US
dc.relation.journalCritical Careen_US
dc.identifier.mitlicensePUBLISHER_CC
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2025-07-18T15:34:43Z
dc.language.rfc3066en
dc.rights.holderThe Author(s)
dspace.date.submission2025-07-18T15:34:43Z
mit.journal.volume29en_US
mit.licensePUBLISHER_CC
mit.metadata.statusAuthority Work and Publication Information Neededen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record