Evidence map›Paper›PMID 40598327›Full record

ArticleBMC nursing2025

Perceived worries in the adoption of artificial intelligence among nurses in neonatal intensive care units.

Ahmad Ayed, Ahmad Batran, Ibrahim Aqtam, Malakeh Z Malak, Moath Abu Ejheisheh, Mosaab Farajallah, Lamees Farraj, Sanaa Alkhatib

Abstract read
In one paragraph

Article in BMC nursing, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 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

8 authors.

Ahmad AyedFaculty of Nursing, Arab American University, Jenin, Palestine.
Ahmad BatranFaculty of Allied Medical Sciences, Department of Nursing, Palestine Ahliya University, Bethlehem, Palestine.
Ibrahim AqtamDepartment of Nursing, Ibn Sina College for Health Professions, Nablus University for Vocational and Technical Education, Nablus, Palestine. ibrahim.aqtam@nu-vte.edu.ps.
Malakeh Z MalakCommunity Health Nursing, Faculty of Nursing, Al-Zaytoonah University of Jordan, Amman, Jordan.
Moath Abu EjheishehFaculty of Allied Medical Sciences, Department of Nursing, Palestine Ahliya University, Bethlehem, Palestine.
Mosaab FarajallahFaculty of Allied Medical Sciences, Department of Nursing, Palestine Ahliya University, Bethlehem, Palestine.
Lamees FarrajFaculty of Allied Medical Sciences, Department of Nursing, Palestine Ahliya University, Bethlehem, Palestine.
Sanaa AlkhatibFaculty of Allied Medical Sciences, Department of Nursing, Palestine Ahliya University, Bethlehem, Palestine.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionArtificial Intelligence (AI) comprises computational algorithms designed to analyze data, learn patterns, and execute tasks traditionally requiring human cognition. These models can support public health initiatives, expedite clinical care, and improve diagnosis accuracy. Thus, artificial intelligence in healthcare sectors has the potential to enhance nursing care by assisting nurses with tasks like documentation, workflow improvement, and decision-making, while reducing workforce stress. This study, guided by the Technology Acceptance Model (TAM), assesses perceived worries regarding AI adoption among nurses in neonatal intensive care units (NICUs).

methodsA cross-sectional quantitative design was employed using convenience sampling. Data were collected using the Worries of Applying AI in Healthcare Questionnaire (WAAI-HCQ) from 227 NICU nurses across nine hospitals in the West Bank (January 2-March 3, 2025). SPSS version 26 was used for analysis.

resultsParticipants demonstrated intermediate levels of AI awareness (M = 2.7, SD = 0.5) and limited prior AI experience (M = 2.3, SD = 0.5). Total AI-related worries were moderate (M = 3.2, SD = 0.9), with healthcare provider-related concerns being highest. Multiple linear regression (R² = 0.846) identified education level (B = 0.074, p = 0.026), AI awareness (B = 2.006, p < 0.001), and AI experience (B = -0.959, p < 0.001) as significant predictors, explaining 84.6% of the variance in AI-related worries.

conclusionsNICU nurses in Palestine exhibit moderate AI awareness and concerns, highlighting the need for targeted education and training to address knowledge gaps and facilitate AI integration. This study contributes new knowledge specifically for conflict-affected, resource-constrained NICU settings, where AI implementation faces unique challenges. CLINICAL TRIAL NUMBER: Not applicable.

Indexed as

Artificial intelligenceHealthcareNeonatal intensive careNursesTechnology adoption

Identifiers

PMID40598327
PMCPMC12211121

What OpenQuestion holds

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LicenceCC BY
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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 OpenQuestion graph.