Artificial intelligence in anaesthesiology: why don't we have it in our hands after a decade of innovation? A systematic review and perspective.

BACKGROUND: Machine 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. METHODS: A 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. RESULTS: Out 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. CONCLUSIONS: Machine 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.