ArticleNursing in critical care2026
The Role of Machine Learning and Artificial Intelligence in Enhancing Critical Care Nursing Practice: A Scoping Review.
Article in Nursing in critical care, 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.
- The Role of Machine Learning and Artificial Intelligence in Enhancing Critical Care Nursing Practice: A Scoping Review.Nursing in critical care · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence (AI) and machine learning (ML) are emerging as transformative tools in healthcare, with significant potential to enhance nursing practice, particularly in intensive care units (ICUs). ICUs pose complex challenges, including high patient acuity, ICU delirium, and nurse workload. These factors demand innovative technological solutions.
aimThis scoping review comprehensively explores the current picture of AI and ML applications in critical care nursing, focusing on decision support systems, predictive analytics, workflow automation, and patient engagement tools.
methodsA search of Four databases (Scopus, PubMed/MEDLINE, Science Direct, and CINAHL) was conducted for original peer-reviewed studies published between January 2019 and September 2025. The 2019 start date was selected to capture the contemporary wave of AI applications in critical care nursing, coinciding with the documented exponential growth in AI-related ICU publications following widespread EHR adoption and the maturation of deep learning architectures.
resultsFive key themes were identified: predictive analytics and early warning systems, clinical decision-support tools, automation and workflow enhancements, monitoring combined with human-AI collaboration, and implementation challenges. Findings reveal that AI can reduce administrative burden and improve care quality. However, significant gaps persist, especially in evaluating long-term outcomes, nurse involvement, and ethical implementation.
conclusionThis scoping review provides a contemporary, integrated thematic synthesis of machine learning and AI applications in critical care nursing. While not claiming absolute novelty, this review addresses a distinct and timely gap by simultaneously mapping predictive analytics, clinical decision support, workflow automation, and implementation challenges within a single evidence synthesis. RELEVANCE TO CLINICAL PRACTICE: AI and machine learning may support critical care nurses by facilitating earlier recognition of patient deterioration, strengthening clinical decision-making, and reducing repetitive workload. Successful implementation requires nurse involvement in system design, appropriate training, transparent algorithms, and integration with existing clinical workflows.
Indexed as
Identifiers
What Socratic 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 Socratic graph.