Evidence map›Paper›PMID 42400781›Full record

ArticleJournal of clinical monitoring and computing2026

Perceptions on artificial intelligence among anaesthesia and intensive care professionals: An international survey on attitudes, expectations and needs.

Raphael Theilen, Tim Kramer, Martin Scharffenberg, Ian-Christopher Jung, Maria Zerlik, Marcelo Gama de Abreu, Saman Sepehr, Thea Koch, Brita Sedlmayr, Robert Huhle and 1 more

Abstract read
In one paragraph

Article in Journal of clinical monitoring and computing, 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

11 authors.

Raphael TheilenDepartment of Anaesthesiology and Intensive Care Medicine, Pulmonary Engineering Group, Faculty of Medicine, University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.
Tim KramerDepartment of Anaesthesiology and Intensive Care Medicine, Pulmonary Engineering Group, Faculty of Medicine, University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.
Martin ScharffenbergDepartment of Anaesthesiology and Intensive Care Medicine, Pulmonary Engineering Group, Faculty of Medicine, University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.
Ian-Christopher JungInstitute for Medical Informatics and Biometry, Faculty of Medicine, University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Fetscherstraße 74, 01307, Dresden, Germany.
Maria ZerlikInstitute for Medical Informatics and Biometry, Faculty of Medicine, University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Fetscherstraße 74, 01307, Dresden, Germany.
Marcelo Gama de AbreuDepartment of Anaesthesiology and Intensive Care Medicine, Pulmonary Engineering Group, Faculty of Medicine, University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.
Saman SepehrEuropean Society of Anaesthesiology and Intensive Care, Brussels, Belgium.
Thea KochDepartment of Anaesthesiology and Intensive Care Medicine, Pulmonary Engineering Group, Faculty of Medicine, University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.
Brita SedlmayrInstitute for Medical Informatics and Biometry, Faculty of Medicine, University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Fetscherstraße 74, 01307, Dresden, Germany.
Robert HuhleDepartment of Anaesthesiology and Intensive Care Medicine, Pulmonary Engineering Group, Faculty of Medicine, University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.
Jakob WittensteinDepartment of Anaesthesiology and Intensive Care Medicine, Pulmonary Engineering Group, Faculty of Medicine, University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany. jakob.wittenstein@ukdd.de.ORCID http://orcid.org/0000-0003-4397-1467

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background Artificial intelligence (AI) can enhance diagnostics, treatment, and workflow efficiency. However, successful integration into clinical practice depends on users' acceptance. Objective To investigate benefits, barriers, and challenges of AI applications among anaesthesia and intensive care professionals. Design International online survey. Main outcome measures The survey included items on familiarity and experiences with AI applications, perceived benefits, concerns, and demographic variables. Descriptive analyses, fisher exact tests, χ²-tests, odds ratios, and Spearman rank correlations were used to explore associations between responses and demographics. Results The survey was distributed by the European Society of Anaesthesiology and Intensive Care in 2023. A total of 510 respondents completed the entire survey, primarily from Europe (78%) and Asia (14.5%), and the majority were board-certified anaesthesiologists (86.3%). 86.5% of the respondents were aware of AI applications, but only 36.8% reported regular encounters. Familiarity was higher among males and intensive care specialists. 94.5% expressed interest in AI training, particularly younger and less experienced professionals. 94.7% expressed willingness to use AI applications, citing benefits such as improved decision-making (92.7%), complication anticipation (88.6%), and workload reduction (80.1%). Younger and female respondents were more optimistic about AI's benefits. Key concerns included lack of explainability (68.4%), over-reliance on AI (80.8%), and medico-legal uncertainties (58.4%). Scepticism was attributed to insufficient validation studies and fears of inaccurate outputs, particularly among experienced clinicians. Conclusions This international survey shows cautious optimism among anaesthesia and intensive care professionals regarding AI applications. Adoption in clinical practice requires tailored training that accounts also for demographic-specific concerns, robust validation, and clear ethical and legal frameworks.

Indexed as

AnesthesiaAnesthesiologyArtificial IntelligenceAttitude of Health PersonnelCritical CareAdultEuropeFemaleHumansInternationalityMaleMiddle AgedPerceptionSurveys and QuestionnairesAnaesthesiologyArtificial IntelligenceDecision Support System, Intensive Care Medicine

Identifiers

PMID42400781
PMCPMC13562144

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.