Evidence map›Paper›PMID 42244824›Full record

ArticleEuropean journal of anaesthesiology and intensive care2026

Automation in perioperative medicine: perceptions, requirements and boundaries - a mixed methods study.

Jeffrey David Iqbal, Maximilian Lindholz, Stefan J Schaller, Martin Schlaepfer

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Article in European journal of anaesthesiology and intensive care, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

4 authors.

Jeffrey David IqbalFrom the Faculty of Medicine, University of Zurich, Zurich, Switzerland (JDI, MS), Berlin Institute of Health at Charité (BIH), Berlin, Germany (ML), Charité - Universitätsmedizin Berlin, Institute of Medical Informatics, Berlin, Germany (SJS), University Hospital Zurich, Institute of Anesthesiology and Perioperative Medicine, Zurich, Switzerland (JDI, MS), Charité - Universitätsmedizin Berlin, Department of Anesthesiology and Intensive Care Medicine (CCM/CVK), Chariteplatz 1, Berlin, Germany (ML, SJS), Charité - Universitätsmedizin Berlin, Department of Radiology, Berlin, Germany (ML), Medical University of Vienna, Department of Anaesthesia, Intensive Care Medicine and Pain Medicine, Division of General Anaesthesia and Intensive Care Medicine, Vienna, Austria (SJS).
Maximilian LindholzFrom the Faculty of Medicine, University of Zurich, Zurich, Switzerland (JDI, MS), Berlin Institute of Health at Charité (BIH), Berlin, Germany (ML), Charité - Universitätsmedizin Berlin, Institute of Medical Informatics, Berlin, Germany (SJS), University Hospital Zurich, Institute of Anesthesiology and Perioperative Medicine, Zurich, Switzerland (JDI, MS), Charité - Universitätsmedizin Berlin, Department of Anesthesiology and Intensive Care Medicine (CCM/CVK), Chariteplatz 1, Berlin, Germany (ML, SJS), Charité - Universitätsmedizin Berlin, Department of Radiology, Berlin, Germany (ML), Medical University of Vienna, Department of Anaesthesia, Intensive Care Medicine and Pain Medicine, Division of General Anaesthesia and Intensive Care Medicine, Vienna, Austria (SJS).
Stefan J SchallerFrom the Faculty of Medicine, University of Zurich, Zurich, Switzerland (JDI, MS), Berlin Institute of Health at Charité (BIH), Berlin, Germany (ML), Charité - Universitätsmedizin Berlin, Institute of Medical Informatics, Berlin, Germany (SJS), University Hospital Zurich, Institute of Anesthesiology and Perioperative Medicine, Zurich, Switzerland (JDI, MS), Charité - Universitätsmedizin Berlin, Department of Anesthesiology and Intensive Care Medicine (CCM/CVK), Chariteplatz 1, Berlin, Germany (ML, SJS), Charité - Universitätsmedizin Berlin, Department of Radiology, Berlin, Germany (ML), Medical University of Vienna, Department of Anaesthesia, Intensive Care Medicine and Pain Medicine, Division of General Anaesthesia and Intensive Care Medicine, Vienna, Austria (SJS).
Martin SchlaepferFrom the Faculty of Medicine, University of Zurich, Zurich, Switzerland (JDI, MS), Berlin Institute of Health at Charité (BIH), Berlin, Germany (ML), Charité - Universitätsmedizin Berlin, Institute of Medical Informatics, Berlin, Germany (SJS), University Hospital Zurich, Institute of Anesthesiology and Perioperative Medicine, Zurich, Switzerland (JDI, MS), Charité - Universitätsmedizin Berlin, Department of Anesthesiology and Intensive Care Medicine (CCM/CVK), Chariteplatz 1, Berlin, Germany (ML, SJS), Charité - Universitätsmedizin Berlin, Department of Radiology, Berlin, Germany (ML), Medical University of Vienna, Department of Anaesthesia, Intensive Care Medicine and Pain Medicine, Division of General Anaesthesia and Intensive Care Medicine, Vienna, Austria (SJS).

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIntegrating artificial intelligence (AI) and automation can potentially advance perioperative medicine, especially in the maintenance phase of general anaesthesia. Recent technological advancements, rising costs, a shortage of anaesthesiologists and the potential for improved quality make a strong case for increased utilisation of these innovations. Despite instances where machines have outperformed humans, automated anaesthesia systems have not been widely adopted.

objectivesThis research paper aims to explore opportunities, challenges, restrictions and requirements for the further automation of anaesthesia, focusing on the intraoperative maintenance phase of anaesthesia. Addressing this gap is crucial for realising the full potential of AI and automation in perioperative care, ultimately leading to improved patient outcomes and healthcare efficiency.

designThis explorative mixed methods study is based on expert interviews, including both qualitative and quantitative survey components.

settingTwo university medical centres in Switzerland (University Hospital Zurich) and Germany (Charité University Medicine Berlin).

participantsForty-two anaesthesiologists (24 residents, 18 consultants) with a mean tenure of 8.6 years (3.5 for residents and 15.4 for consultants; range 1-29 years) were interviewed.

main outcome measuresThe primary endpoints were thematic descriptions due to the exploratory nature of this study.

resultsAnaesthesiologists interviewed welcome AI, list automation aspects as desired functionalities and see its potential for error reduction. Attitudes towards automation were influenced by the level of automation involved. While augmentative support received unanimous approval, replacement scenarios with high machine autonomy were seen more critically despite diverse opinions. Several opportunities and challenges were identified for stakeholder groups such as anaesthesiologists, nurses, patients, surgeons, hospitals and payers. Further, requirements and restrictions were identified to which automation systems should be subject.

conclusionsResidents and board-certified anaesthesiologists are convinced that technology, including AI and automation, has the potential to address challenges in anaesthesia practice. They list concrete requirements and restrictions for their use and expect a range of opportunities and challenges. With increased adoption, the roles and responsibilities of anaesthesiologists and other perioperative healthcare providers will change. Given both adoption and technological barriers, the complete replacement of anaesthesiologists by machines seems unlikely in the mid-term.

Identifiers

PMID42244824
PMCPMC13232904

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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.