Systematic review on nursing AI
A recent open‑access systematic review summarizes how AI technologies are being used in nursing clinical decision‑making, cataloguing models and study designs across the field. The paper is available through a high‑engagement social post and can serve as a reference for where nursing AI evidence currently exists. (x.com)
Artificial intelligence in nursing works like a prediction and recommendation layer on top of patient data, and a new review finds the evidence is still thin in day-to-day care. (pmc.ncbi.nlm.nih.gov) The review, published open access in the *Journal of Clinical Nursing*, searched CINAHL, PubMed, Scopus, ProQuest and Medic for experimental studies published from 2005 through 2024. It identified eight studies on artificial intelligence tools used to support nurses’ clinical decision-making in healthcare settings. (pmc.ncbi.nlm.nih.gov) Those eight studies reported gains in areas nurses handle every shift: discharge planning, spotting patient deterioration, neonatal resuscitation, seizure assessment, pressure-ulcer prevention and documentation quality. In one study, a discharge support system cut 30-day readmissions from 22.2% to 9.4%; in another, neonatal resuscitation accuracy reached 94% to 95%, versus 55% to 80% without the tool. (pmc.ncbi.nlm.nih.gov) Clinical decision-making in nursing means choosing what to monitor, when to escalate, what care to prioritize and how to document it. Artificial intelligence systems do that by sorting large amounts of clinical data faster than a person can, then flagging patterns a nurse may want to act on. (pmc.ncbi.nlm.nih.gov) The review lands as hospitals are testing more digital tools while nursing teams face staffing shortages, workload pressure and heavy documentation demands. Its authors said artificial intelligence may improve workflow efficiency and patient care, but only if systems are implemented with attention to safety, trust and accountability. (pmc.ncbi.nlm.nih.gov) A second, broader review in the *Journal of Advanced Nursing* shows how early the field still is. That umbrella review, published in 2026, found 16 literature reviews covering 965 primary nursing artificial intelligence studies, but said real-world testing in clinical nursing settings was rare and rigorous comparative analysis was lacking. (onlinelibrary.wiley.com) That umbrella review also found recurring problems beyond sample size: algorithmic bias, limited reproducibility, too few controlled studies and unresolved patient-data privacy questions. It said nurses were too often absent from the design and development of the systems meant to support their work. (onlinelibrary.wiley.com) The narrower *Journal of Clinical Nursing* review reached a similar conclusion from a different angle. The authors said ethical safeguards need to cover transparency, bias mitigation, data privacy and accountability in artificial-intelligence-driven decisions before wider adoption. (onlinelibrary.wiley.com) The picture that emerges is not of nurses being replaced, but of a field still trying to prove which tools help at the bedside and which only perform well in controlled studies. For now, the strongest contribution of the new review may be to show exactly how small the evidence base remains. (pmc.ncbi.nlm.nih.gov)
