SynthesisJournal of clinical nursing2026
Artificial Intelligence Technologies Supporting Nurses' Clinical Decision-Making: A Systematic Review.
Synthesis in Journal of clinical nursing, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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
11 citing papers in PubMed.
- Whose Knowledge Counts in Labour? Obstetric Nurses Negotiating Clinical Judgement, Algorithmic Authority and Woman-Centred Care in Artificial Intelligence-Enhanced Foetal Monitoring.Nursing inquiry · 2026Article
- Development and Validation of an Interpretable Machine Learning Model for Inpatient Fall Risk Using Electronic Health Record Data.Nursing reports (Pavia, Italy) · 2026Article
- Relationship Between Nurses' Decision-Making Styles and Stage-Specific Missed Care in Oncology: A Nursing Process Perspective.Journal of clinical nursing · 2026Article
- Review
- Profiles of artificial intelligence literacy and associations with evidence-based practice competence among nurses: a latent profile analysis.BMC nursing · 2026Article
- DNP-PhD: A transformative shift.The Nurse practitioner · 2026Review
- Association between artificial intelligence literacy and empathic behavior among nursing interns: adjustment for moral distress and moral resilience.Frontiers in medicine · 2026Article
- The Mediating and Moderating Roles of Innovation Climate in the Relationship Between Nurses' Attitudes Toward Artificial Intelligence, Creative Self-Efficacy, and Clinical Competence.Journal of nursing management · 2026Article
- Intention to Use Large Language Models Among Clinical Nurses in China With Prior Familiarity With or Experience Using LLMs: A Qualitative Study.Journal of nursing management · 2026Article
- A nursing perspective on human-AI collaboration in personalized breast cancer care pathways.Frontiers in oncology · 2026Article
- Artificial Intelligence in Nursing Governance and Regulation: An Umbrella Review of Ethical and Policy Dimensions.SAGE open nursingReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
backgroundThe use of technology to support nurses' decision-making is increasing in response to growing healthcare demands. AI, a global trend, holds great potential to enhance nurses' daily work if implemented systematically, paving the way for a promising future in healthcare.
objectivesTo identify and describe AI technologies for nurses' clinical decision-making in healthcare settings.
designA systematic literature review. DATA SOURCES: CINAHL, PubMed, Scopus, ProQuest, and Medic were searched for studies with experimental design published between 2005 and 2024. REVIEW
methodsJBI guidelines guided the review. At least two researchers independently assessed the eligibility of the studies based on title, abstract, and full text, as well as the methodological quality of the studies. Narrative analysis of the study findings was performed.
resultsEight studies showed AI tools improved decision-making, patient care, and staff performance. A discharge support system reduced 30-day readmissions from 22.2% to 9.4% (p = 0.015); a deterioration algorithm cut time to contact senior staff (p = 0.040) and order tests (p = 0.049). Neonatal resuscitation accuracy rose to 94%-95% versus 55%-80% (p < 0.001); seizure assessment confidence improved (p = 0.01); pressure ulcer prevention (p = 0.002) and visual differentiation (p < 0.001) improved. Documentation quality increased (p < 0.001).
conclusionsAI integration in nursing has the potential to optimise decision-making, improve patient care quality, and enhance workflow efficiency. Ethical considerations must address transparency, bias mitigation, data privacy, and accountability in AI-driven decisions, ensuring patient safety and trust while supporting equitable, evidence-based care delivery. IMPACT: The findings underline the transformative role of AI in addressing pressing nursing challenges such as staffing shortages, workload management, and error reduction. By supporting clinical decision-making and workflow efficiency, AI can enhance patient safety, care quality, and nurses' capacity to focus on direct patient care. A stronger emphasis on research and implementation will help bridge usability and scalability gaps, ensuring sustainable integration of AI across diverse healthcare settings.
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.