ReviewDigital health
Artificial intelligence applications in intensive care unit nursing: A narrative review (2020-2025).
Review in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Ventilator-derivedIn vivo (Athens, Greece)Pooled it
- Navigating AI governance for oncology nursing: Existing models, implications, and a Call for nurse-led oversight in Asia Pacific health systems.Asia-Pacific journal of oncology nursing · 2026Article
- Smart Cardiac ICU: Digital Integration, Predictive Analytics, and Perioperative Inflammation.Bioengineering (Basel, Switzerland) · 2026Review
- Technology use in pediatric cardiovascular surgery intensive care nursing: a qualitative study.BMC nursing · 2026Article
- Perceptions of Intensive Care Nurses Toward Artificial Intelligence Technologies: A Qualitative Study.Nursing & health sciences · 2026Article
- Navigating Professional Accountability in AI-Assisted Nursing Practice: Ethical and Legal Imperatives for the Digital Age.SAGE open nursingArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aim: To synthesize recent research on artificial intelligence (AI) in intensive care unit (ICU) nursing from 2020 to 2025, highlight trends, and outline integration challenges. Methods: A narrative synthesis approach was used, reviewing English-language studies from PubMed, Web of Science, Scopus, and IEEE Xplore. From 4138 articles, 37 studies were included. Results: Evidence was international with strong contributions from Asia and North America. Most studies were retrospective and drew on large ICU databases such as MIMIC-III/IV and eICU. Methods were dominated by machine learning, with limited but growing deep learning. Applications clustered around early warning and risk prediction, with additional work on nursing decision support and workload or documentation support. Reported discrimination frequently exceeded AUC 0.80, while calibration, external validation, and human factors evaluation were less often described. Conclusion: Artificial intelligence shows promise for earlier risk recognition, decision support, and workflow enablement in ICU nursing. Priorities include multicenter prospective evaluation, external validation with calibration, electronic health record-embedded implementation, and nurse codesign to ensure safe, useful, and generalizable tools. Implications for clinical practice: Thoughtfully integrated AI can support timely decisions and reduce documentation burden when paired with real-time validation and nurse-led workflow adaptation.
Indexed as
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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.