ArticleBMJ open2026
Artificial intelligence in perioperative pain: a scoping review protocol.
Article in BMJ open, 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.
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionPerioperative pain is a major determinant of patients' experience and may influence long-term outcomes. Artificial intelligence (AI) is applied to perioperative datasets to predict acute postoperative pain, opioid requirements, analgesic-related adverse events, chronic postsurgical pain and pain trajectories. However, the evidence is dispersed across perioperative phases, clinical outcomes, data modalities and AI methodologies, while the extent to which current models address validation, interpretability, uncertainty and clinical implementation remains unclear. This scoping review protocol aims to characterise the existing evidence on AI applications related to perioperative pain to identify methodological features that affect its clinical credibility and implementation. METHODS AND ANALYSIS: This protocol will follow the Joanna Briggs Institute methodology for scoping reviews and will be reported in line with Preferred Reporting Items for Systematic Reviews and Meta-Analysis Extension for Scoping Reviews (PRISMA-ScR) guidance. Eligible studies will include human research evaluating AI methods in relation to pain-related perioperative outcomes, including observational studies, interventional studies and predictive modelling studies. AI approaches include machine learning, deep learning, natural language processing, computer vision, large language models and hybrid or ensemble methods. Searches will be conducted from PubMed/MEDLINE, Embase, the Cochrane Library, medRxiv, arXiv and ClinicalTrials.gov, with supplementary screening of reference lists. Results will be synthesised descriptively. The formal literature searches are planned for September 2026, with completion of study selection, data charting, evidence synthesis and preparation of the final review expected by December 2026. ETHICS AND DISSEMINATION: We will chart ethical aspects reported in included studies, such as governance of retrospective electronic health record use, consent waivers and privacy protections, where available. Findings will be disseminated through submission to a peer-reviewed journal, presentation at scientific meetings and open sharing of search strategies on the Open Science Framework. PROSPERO REGISTRATION NUMBER: This protocol was prospectively registered on the Open Science Framework in March 2026: Cascella M
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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.