ReviewCureus2025
Machine Learning in Nursing: A Cross-Disciplinary Review.
Review in Cureus, 2025. 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.
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
Rapid advancement of artificial intelligence (AI) and machine learning (ML) is transforming healthcare, and nursing practice is inevitably affected. Yet limited formal education leaves many nurses hesitant to integrate such tools into everyday care. This cross‑disciplinary review (i) introduces the fundamental concepts, core types and common evaluation metrices, illustrated with nursing-specific examples; (ii) catalogues the main algorithmic applications in nursing research; and (iii) describes ethical and practical challenges in their clinical use. A structured search of PubMed, Embase, Scopus, IEEE Xplore and ACM Digital Library (2015-2024) retrieved 1445 records; after de-duplication and screening against inclusion criteria (peer-reviewed, English-language studies that developed, implemented or evaluated an ML method in a clinical, community-health or educational nursing context), 61 papers were analyzed. Supervised approaches predominated, while unsupervised and semi-supervised techniques were less common. Models were evaluated mainly with accuracy, area under the receiver operating characteristic curve (AUROC), precision-recall and F1-score for classification, or mean-absolute/squared error for regression. Applications spanned eight main categories: (1) predictive risk assessment and early‑warning systems; (2) clinical decision support and diagnostic aid; (3) continuous patient monitoring; (4) workflow, staffing and operational optimizations; (5) documentation and information extraction via natural‑language processing; (6) education and competency development; and (7) other niche applications. Key barriers to wider adoption remain regulatory and ethical constraints, data quality, model transparency and engagement issues, data challenges, lack of ML-specific training in nursing curricula, and operational limitations. The utilization of ML in nursing is based on three core pillars: strong interdisciplinary collaboration, systematic integration of ML at all levels of nursing education, and a guiding framework that maps human-centered nursing interventions to interpretable ML tools. Such a foundation will enable nurses to safely leverage the results of algorithms, avoid biases and risks, and integrate ethical responsibility into technology-enhanced care.
Indexed as
Identifiers
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.