Evidence map›Paper›PMID 39765983›Full record

ReviewHealthcare (Basel, Switzerland)2024

Artificial Intelligence in Nursing: Technological Benefits to Nurse's Mental Health and Patient Care Quality.

Hamad Ghaleb Dailah, Mahdi Koriri, Alhussean Sabei, Turky Kriry, Mohammed Zakri

Abstract readReview
In one paragraph

Review in Healthcare (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.

0numbers the graph read from it
0cells of the map it votes in
38citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

38 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Hamad Ghaleb DailahCollege of Nursing and Health Sciences, Jazan University, Jazan 45142, Saudi Arabia.ORCID 0000-0002-3019-0461
Mahdi KoririCollege of Nursing and Health Sciences, Jazan University, Jazan 45142, Saudi Arabia.
Alhussean SabeiCollege of Nursing and Health Sciences, Jazan University, Jazan 45142, Saudi Arabia.
Turky KriryCollege of Nursing and Health Sciences, Jazan University, Jazan 45142, Saudi Arabia.
Mohammed ZakriCollege of Nursing and Health Sciences, Jazan University, Jazan 45142, Saudi Arabia.

Funding

Jazan University GSSRD-24
6 · The paper itself

Abstract

Nurses are frontline caregivers who handle heavy workloads and high-stakes activities. They face several mental health issues, including stress, burnout, anxiety, and depression. The welfare of nurses and the standard of patient treatment depends on resolving this problem. Artificial intelligence is revolutionising healthcare, and its integration provides many possibilities in addressing these concerns. This review examines literature published over the past 40 years, concentrating on AI integration in nursing for mental health support, improved patient care, and ethical issues. Using databases such as PubMed and Google Scholar, a thorough search was conducted with Boolean operators, narrowing results for relevance. Critically examined were publications on artificial intelligence applications in patient care ethics, mental health, and nursing and mental health. The literature examination revealed that, by automating repetitive chores and improving workload management, artificial intelligence (AI) can relieve mental health challenges faced by nurses and improve patient care. Practical implications highlight the requirement of using rigorous implementation strategies that address ethical issues, data privacy, and human-centred decision-making. All changes must direct the integration of artificial intelligence in nursing to guarantee its sustained and significant influence on healthcare.

Indexed as

artificial intelligence in nursingdigital healthnurse wellbeingpatient care

Identifiers

PMID39765983
PMCPMC11675209

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.