SynthesisIntensive care medicine2021
Moving from bytes to bedside: a systematic review on the use of artificial intelligence in the intensive care unit.
Synthesis in Intensive care medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 140 papers, 9 of them syntheses that pooled it.
What it found
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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
140 citing papers in PubMed, 9 syntheses or guidelines pooled it.
- Pooled it
- Methodological conduct and risk of bias in studies on prenatal birthweight prediction models using machine learning techniques: a systematic review.BMC pregnancy and childbirth · 2025Pooled it
- Operationalization of Artificial Intelligence Applications in the Intensive Care Unit: A Systematic Review.JAMA network open · 2025Pooled it
- From bytes to bedside: a systematic review on the use and readiness of artificial intelligence in the neonatal and pediatric intensive care unit.Intensive care medicine · 2024Pooled it
- Using Artificial Intelligence to Predict Intracranial Hypertension in Patients After Traumatic Brain Injury: A Systematic Review.Neurocritical care · 2024Pooled it
- Artificial intelligence in the risk prediction models of cardiovascular disease and development of an independent validation screening tool: a systematic review.BMC medicine · 2024Pooled it
- Computational physiological models for individualised mechanical ventilation: a systematic literature review focussing on quality, availability, and clinical readiness.Critical care (London, England) · 2023Pooled it
- Artificial intelligence for mechanical ventilation: systematic review of design, reporting standards, and bias.British journal of anaesthesia · 2022Pooled it
- Moving from bytes to bedside: a systematic review on the use of artificial intelligence in the intensive care unit.Intensive care medicine · 2021Pooled it
- Discussing expected long-term quality of life in the ICU: effect on experiences and outcomes of patients, family, and clinicians-a randomized clinical trial.Intensive care medicine · 2025Trial
- Intracranial pressure physiology, monitoring and individualized management in the acute brain injured patient.Intensive care medicine · 2026Review
- Integrating Artificial Intelligence in Nursing Practice With Decubitus Risk Prediction Alerts: A Pilot Process Evaluation.Journal of clinical nursing · 2026Article
- Perceptions on artificial intelligence among anaesthesia and intensive care professionals: An international survey on attitudes, expectations and needs.Journal of clinical monitoring and computing · 2026Article
- Observational
- The Role of Machine Learning and Artificial Intelligence in Enhancing Critical Care Nursing Practice: A Scoping Review.Nursing in critical care · 2026Article
- Machine Learning-Based Prediction of 48-Hour Extubation Success in Mechanically Ventilated Children: A Single-Center Retrospective Cohort Study.Children (Basel, Switzerland) · 2026Article
- Smart Cardiac ICU: Digital Integration, Predictive Analytics, and Perioperative Inflammation.Bioengineering (Basel, Switzerland) · 2026Review
- Systematic review and meta-analysis of AI in lung cancer metastasis imaging for diagnosis and prognosis.NPJ digital medicine · 2026Article
- Healthcare professionals show high AI enthusiasm but limited knowledge: A cross-sectional study.PLOS digital health · 2026Article
- Multi-source data integration through pooling and transfer learning improves generalizability and specialization of deep learning models for ICU mortality and length of stay prediction: a four-database external validation study.Critical care (London, England) · 2026Article
80 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeDue to the increasing demand for intensive care unit (ICU) treatment, and to improve quality and efficiency of care, there is a need for adequate and efficient clinical decision-making. The advancement of artificial intelligence (AI) technologies has resulted in the development of prediction models, which might aid clinical decision-making. This systematic review seeks to give a contemporary overview of the current maturity of AI in the ICU, the research methods behind these studies, and the risk of bias in these studies.
methodsA systematic search was conducted in Embase, Medline, Web of Science Core Collection and Cochrane Central Register of Controlled Trials databases to identify eligible studies. Studies using AI to analyze ICU data were considered eligible. Specifically, the study design, study aim, dataset size, level of validation, level of readiness, and the outcomes of clinical trials were extracted. Risk of bias in individual studies was evaluated by the Prediction model Risk Of Bias ASsessment Tool (PROBAST).
resultsOut of 6455 studies identified through literature search, 494 were included. The most common study design was retrospective [476 studies (96.4% of all studies)] followed by prospective observational [8 (1.6%)] and clinical [10 (2%)] trials. 378 (80.9%) retrospective studies were classified as high risk of bias. No studies were identified that reported on the outcome evaluation of an AI model integrated in routine clinical practice.
conclusionThe vast majority of developed ICU-AI models remain within the testing and prototyping environment; only a handful were actually evaluated in clinical practice. A uniform and structured approach can support the development, safe delivery, and implementation of AI to determine clinical benefit in the ICU.
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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.