Evidence map›Paper›PMID 40197527›Full record

ArticleBMC nursing2025

Neonatal nurses' experiences with generative AI in clinical decision-making: a qualitative exploration in high-risk nicus.

Abeer Nuwayfi Alruwaili, Afrah Madyan Alshammari, Ali Alhaiti, Nadia Bassuoni Elsharkawy, Sayed Ibrahim Ali, Osama Mohamed Elsayed Ramadan

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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 20 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 3 pooled it
–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

20 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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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

6 authors.

Abeer Nuwayfi AlruwailiCollege of Nursing, Nursing Administration and Education Department, Jouf University, Sakaka, 72388, Saudi Arabia. analrwili@ju.edu.sa.
Afrah Madyan AlshammariCollege of Nursing, Department of Maternity and Pediatric Health Nursing, Jouf University, Sakaka, 72388, Saudi Arabia.
Ali AlhaitiDepartment of Nursing, College of Applied Sciences, Almaarefa University, Diriyah, Riyadh, 13713, Saudi Arabia.
Nadia Bassuoni ElsharkawyCollege of Nursing, Department of Maternity and Pediatric Health Nursing, Jouf University, Sakaka, 72388, Saudi Arabia.
Sayed Ibrahim AliCollege of Medicine, Department of Family and Community Medicine, King Faisal University, Alhssa, 31982, Saudi Arabia.
Osama Mohamed Elsayed RamadanPediatric Nursing Department, Faculty of Nursing, Cairo University, Cairo, 11562, Egypt. osama_ramadan_85@cu.edu.eg.ORCID https://orcid.org/0000-0002-9616-8590

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundNeonatal nurses in high-risk Neonatal Intensive Care Units (NICUs) navigate complex, time-sensitive clinical decisions where accuracy and judgment are critical. Generative artificial intelligence (AI) has emerged as a supportive tool, yet its integration raises concerns about its impact on nurses' decision-making, professional autonomy, and organizational workflows.

aimThis study explored how neonatal nurses experience and integrate generative AI in clinical decision-making, examining its influence on nursing practice, organizational dynamics, and cultural adaptation in Saudi Arabian NICUs.

methodsAn interpretive phenomenological approach, guided by Complexity Science, Normalization Process Theory, and Tanner's Clinical Judgment Model, was employed. A purposive sample of 33 neonatal nurses participated in semi-structured interviews and focus groups. Thematic analysis was used to code and interpret data, supported by an inter-rater reliability of 0.88. Simple frequency counts were included to illustrate the prevalence of themes but were not used as quantitative measures. Trustworthiness was ensured through reflexive journaling, peer debriefing, and member checking.

resultsFive themes emerged: (1) Clinical Decision-Making, where 93.9% of nurses reported that AI-enhanced judgment but required human validation; (2) Professional Practice Transformation, with 84.8% noting evolving role boundaries and workflow changes; (3) Organizational Factors, as 97.0% emphasized the necessity of infrastructure, training, and policy integration; (4) Cultural Influences, with 87.9% highlighting AI's alignment with family-centered care; and (5) Implementation Challenges, where 90.9% identified technical barriers and adaptation strategies.

conclusionsGenerative AI can support neonatal nurses in clinical decision-making, but its effectiveness depends on structured training, reliable infrastructure, and culturally sensitive implementation. These findings provide evidence-based insights for policymakers and healthcare leaders to ensure AI integration enhances nursing expertise while maintaining safe, patient-centered care.

Indexed as

Clinical decision-makingCultural contextGenerative artificial intelligenceNeonatal nursingProfessional practiceSaudi Arabia

Identifiers

PMID40197527
PMCPMC11977934

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.