SynthesisBritish journal of anaesthesia2024
Machine learning-augmented interventions in perioperative care: a systematic review and meta-analysis.
Synthesis in British journal of anaesthesia, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Implementation determinants and outcomes of artificial intelligence in surgical healthcare: a systematic review protocol.BMJ open · 2026Article
- From Intelligent Operating Room to Smart ICU: Digital Continuity, AI-Supported Decision-Making and CRRT as a Model of Pharmacokinetic Personalization in Critical Care.Healthcare (Basel, Switzerland) · 2026Review
- Review
- Comparison of large language models and conventional machine learning in postoperative outcome prediction: a retrospective, multi-national development and validation study.Korean journal of anesthesiology · 2026Article
- Comments on "Development and evaluation of an automated phenylephrine delivery system by lower-limit control for managing intraoperative hypotension" by Nagata et al.Journal of anesthesia · 2026Article
- Risk prediction in spine surgery: a scoping review of traditional models, artificial intelligence, and the challenge of clinical translation.Spine deformity · 2026Review
- Implementation and learning curve in AI-assisted fluid management during abdominal oncologic surgery: a retrospective observational study.Journal of anesthesia, analgesia and critical care · 2026Article
- The Role of Biomarkers in Personalized Anesthesia: From Physiological Parameters to Molecular Diagnostics.Biomedicines · 2026Review
- Comparing manual vs. automated machine learning and deep learning models for predicting one-year mortality in elderly hip fracture patients.Frontiers in medicine · 2026Article
- Leveraging transformer-based artificial intelligence for enhanced anesthetic decision-making in orthopedic surgery.Frontiers in medicine · 2026Article
- Explainable artificial intelligence in anesthesiology prediction models: bridging the gap from black-box algorithms to clinical decision support.Frontiers in medicine · 2026Review
- Intraoperative Hemodynamic Management and Cerebral Protection During Awake Craniotomy: Evidence-Based Approaches.Anesthesiology research and practice · 2026Review
- Precision perioperative AI: from signals, images, and records to applications in anesthesia-a narrative mini-review proposing an operational framework.Frontiers in medicine · 2026Review
- Article
- Accuracy and Reliability of Artificial Intelligence in Surgical Decision-Making: A Literature Review.Cureus · 2025Review
- Artificial Intelligence in Anesthesia: Enhancing Precision, Safety, and Global Access Through Data-Driven Systems.Journal of clinical medicine · 2025Review
- Review
- Emerging Technology and the Future of Perioperative Care: Perspectives and Recommendations From the 2023 Stoelting Conference of the Anesthesia Patient Safety Foundation.Anesthesia and analgesia · 2025Review
- What is new in cardiac anesthesia in 2024?Journal of anesthesia and translational medicine · 2025Review
- Nociception level index as a tool of measuring pain objectively in patients with complex regional pain syndrome: a feasibility study.Frontiers in pain research (Lausanne, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
backgroundWe lack evidence on the cumulative effectiveness of machine learning (ML)-driven interventions in perioperative settings. Therefore, we conducted a systematic review to appraise the evidence on the impact of ML-driven interventions on perioperative outcomes.
methodsOvid MEDLINE, CINAHL, Embase, Scopus, PubMed, and ClinicalTrials.gov were searched to identify randomised controlled trials (RCTs) evaluating the effectiveness of ML-driven interventions in surgical inpatient populations. The review was registered with PROSPERO (CRD42023433163) and conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Meta-analysis was conducted for outcomes with two or more studies using a random-effects model, and vote counting was conducted for other outcomes.
resultsAmong 13 included RCTs, three types of ML-driven interventions were evaluated: Hypotension Prediction Index (HPI) (n=5), Nociception Level Index (NoL) (n=7), and a scheduling system (n=1). Compared with the standard care, HPI led to a significant decrease in absolute hypotension (n=421, P=0.003, I
conclusionsHPI decreased the duration of intraoperative hypotension, and NoL decreased postoperative pain scores, but no significant impact on other clinical outcomes was found. We highlight the need to address both methodological and clinical practice gaps to ensure the successful future implementation of ML-driven interventions. SYSTEMATIC REVIEW PROTOCOL: CRD42023433163 (PROSPERO).
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