Evidence map›Paper›PMID 41947117›Full record

ArticleBMC medical ethics2026

Decision-making for ICU admission: is there a place for AI? Exploring and understanding meaning, experiences, and perspectives.

Ambre Sauvage, Antoine Guillon, Marie-France Mamzer

Abstract read
In one paragraph

Article in BMC medical ethics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Ambre SauvageParis Cité University, Sorbonne University, Inserm, Cordeliers Research Center, Paris, 75006, France.
Antoine GuillonIntensive Care Unit, Tours University Hospital, Tours, 37000, France. antoine.guillon@CHU-tours.fr.
Marie-France MamzerParis Cité University, Sorbonne University, Inserm, Cordeliers Research Center, Paris, 75006, France.

Funding

Inserm and the French Ministry of Health in the context of MESSIDORE call operated by IReSP GENIALLY, 2022, Inserm-MESSIDORE N° 72
6 · The paper itself

Abstract

backgroundIntensive care unit (ICU) admission (or refusal) decisions are intricate, relying on the assessment of patient benefit by the intensivist, sole decision-maker, without clear guidelines. As artificial intelligence (AI) rapidly develops predictive applications in healthcare, it could serve as a valuable forecasting tool to aid intensivist decision-making. This study aimed to explore whether intensivists are in demand of an “intelligent” decision-support tool for admissions and, if so, their specific expectations.

methodsA cross-sectional qualitative study was conducted. Ten French intensivists were interviewed between February and May 2024 and their contributions were analyzed using grounded theory method.

resultsParticipants varied in experience (1–35 years), region of practice (hyper-urbanized, n = 5, or not) and familiarity with AI (n = 4). Nine and a half hours of interviews revealed that the ICU admission process is a complex undertaking, depending on many criteria and often involving unconventional decision pathways. Intensivists’ decision-making was inherently subjective, deeply tied to moral values aiming to preserve their medical integrity, though sometimes manifesting as biases that result in unfairness. Participants frequently voiced discomfort with the uncertainty in “grey areas”, that is to say poorly documented and non-extreme situations. While intensivists expected AI to counteract their biases and augment their knowledge, they also feared a loss of the humanity in decision-making to the expense of the technical element.

conclusionExploring experiences of intensivists regarding ICU admission revealed that the decision is less a matter of protocol than of nuanced, subjective assessment. AI likely has a role in supporting ICU admission or refusal decisions, provided its limitations as a purely informative statistical tool are acknowledged.

Indexed as

Artificial IntelligenceAttitude of Health PersonnelClinical Decision-MakingDecision MakingIntensive Care UnitsPatient AdmissionAdultCritical CareCross-Sectional StudiesFemaleFranceGrounded TheoryHumansMaleQualitative ResearchArtificial intelligenceDecision makingIntensive carePatient admissionQualitative research

Identifiers

PMID41947117
PMCPMC13270744

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.