SynthesisBMC anesthesiology2026
Artificial intelligence in anaesthesiology: why don't we have it in our hands after a decade of innovation? A systematic review and perspective.
Synthesis in BMC anesthesiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- Predicting patient care pathway deviations and their consequences in anaesthesia using machine learning.Journal of clinical monitoring and computing · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMachine learning (ML) tools are increasingly integrated into various sectors, including healthcare, where they have demonstrated disruptive potential. While anaesthesia is a specialty historically shaped by technological innovation, the precise clinical impact of ML remains poorly defined. Furthermore, despite a decade of prolific model development, the systematic translation of these computational tools into routine, everyday clinical workflows has stagnated.
methodsA systematic literature review was conducted using the PubMed/Medline and EBSCO databases, supplemented by an analysis of Food and Drug Administration (FDA)-approved medical ML applications. To ensure methodological rigour, the selection process followed the PRISMA 2020 guidelines. Eligible peer-reviewed publications were restricted to those focusing primarily on the development, validation, or implementation of machine learning applications within the field of anaesthesia.
resultsOut of 6,425 screened publications, 1,021 were included in a datasheet. These included 302 reviews or opinion pieces, 5 case reports, and 714 articles focused on tool development or clinical evaluation. Although research topics were highly diverse, the vast majority of studies focused on the prediction of perioperative complications and patient prognosis.
conclusionsMachine learning represents a highly active and debated domain in anaesthesia, characterised by a substantial volume of published research. However, exceptionally few algorithmic models have successfully translated into practice-changing clinical tools. This persistent gap indicates that technical innovation alone is insufficient, as translation is severely hindered by inherent model limitations, software interoperability constraints, and socio-technical challenges within the clinical environment. Addressing the scarcity of hybrid clinician-developer profiles and establishing robust, external field validation are critical pre-requisites for meaningful clinical integration.
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Identifiers
What OpenQuestion holds
Registered trials
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.