Evidence map›Paper›PMID 40267422›Full record

ArticleJMIR medical informatics2025

Expectations of Intensive Care Physicians Regarding an AI-Based Decision Support System for Weaning From Continuous Renal Replacement Therapy: Predevelopment Survey Study.

Benjamin Popoff, Sandie Cabon, Marc Cuggia, Guillaume Bouzillé, Thomas Clavier

Abstract read
In one paragraph

Article in JMIR medical informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Observational
  5. Review
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

5 authors.

Benjamin PopoffDepartment of Anesthesiology, Critical Care and Perioperative Medicine, CHU Rouen, 37 Bd Gambetta, Rouen, 76000, France, 33 232888292.ORCID 0000-0003-2854-0909
Sandie CabonUniv Rennes, CHU Rennes, INSERM, LTSI-UMR 1099, Rennes, France.ORCID 0000-0001-7847-0916
Marc CuggiaUniv Rennes, CHU Rennes, INSERM, LTSI-UMR 1099, Rennes, France.ORCID 0000-0001-6943-3937
Guillaume BouzilléUniv Rennes, CHU Rennes, INSERM, LTSI-UMR 1099, Rennes, France.ORCID 0000-0002-3637-6558
Thomas ClavierDepartment of Anesthesiology, Critical Care and Perioperative Medicine, CHU Rouen, 37 Bd Gambetta, Rouen, 76000, France, 33 232888292.ORCID 0000-0001-8630-7537

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Critically ill patients in intensive care units (ICUs) require continuous monitoring, generating vast amounts of data. Clinical decision support systems (CDSS) leveraging artificial intelligence (AI) technologies have shown promise in improving diagnostic, prognostic, and therapeutic decision-making. However, these models are rarely implemented in clinical practice. Objective: The aim of this study was to survey ICU physicians to understand their expectations, opinions, and level of knowledge regarding a proposed AI-based CDSS for continuous renal replacement therapy (CRRT) weaning, a clinical decision-making process that is still complex and lacking in guidelines. This will be used to guide the development of an AI-based CDSS on which our team is working to ensure user-centered design and successful integration into clinical practice. Methods: A prospective cross-sectional survey of French-speaking physicians with clinical activity in intensive care was conducted between December 2023 and April 2024. The questionnaire consisted of 20 questions structured around 4 axes: overview of the problem and current practices concerning weaning from CRRT, opinion on AI-based CDSS, implementation in daily clinical practice, real-life operation and willingness to adopt the CDSS in everyday practice. Statistical analyses included Wilcoxon rank sum tests for quantitative variables and χ2 or Fisher exact tests for qualitative variables, with multivariate analyses performed using ordinal logistic regression. Results: A total of 171 complete responses were received. Physicians expressed an interest in a CDSS for CRRT weaning, with 70.2% (120/171) viewing AI-based CDSS favorably. Opinions were split regarding the difficulty of the weaning decision itself, with 46.2% (79/171) disagreeing that it is challenging, while 31.6% (54/171) agreed. However, 66.1% (113/171) of respondents supported the value of an AI-based CDSS to assist them in this decision, with younger physicians showing stronger support (81.8%, 27/33 vs 62.3%; 86/138; P=.01). Most respondents (163/171, 95.3%) emphasized the importance of understanding the criteria used by the model to make its predictions. Conclusions: Our findings highlight an optimistic attitude among ICU physicians toward AI-based CDSS for CRRT weaning, emphasizing the need for transparency, integration into existing workflows, and alignment with clinicians' decision-making processes. Actionable recommendations include incorporating key variables such as urine output and biological parameters, defining probability thresholds for recommendations and ensuring model transparency to facilitate the successful adoption and integration into clinical practice. The methodology of this survey may help the development of further predevelopment studies accompanying AI-based CDSS projects.

Indexed as

Artificial IntelligenceAttitude of Health PersonnelContinuous Renal Replacement TherapyCritical CareDecision Support Systems, ClinicalPhysiciansAdultCross-Sectional StudiesFemaleHumansIntensive Care UnitsMaleMiddle AgedProspective StudiesSurveys and QuestionnairesAI toolsartificial intelligenceclinical decision makingclinical decision support systemcross-sectional surveydecision makingdecision supportFrenchICUintensive care physiciansphysicianquestionnairerenal replacement therapysurveysurvey studytherapeuticuser-centereduser-centered design

Identifiers

PMID40267422
PMCPMC12043247

What Socratic holds

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Registered trials

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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 Socratic graph.