Cited by 114Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AIArtificial Intelligence Applications

The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index; none of the 24 sources its claims cite is checked for support yet.

Exploring nurses’ awareness and attitudes toward artificial intelligence: Implications for nursing practice

Open access
Alruwaili, Majed Mowanes, Abuadas, Fuad H., Alsadi, Mohammad, Alruwaili, Abeer Nuwayfi, Elsayed Ramadan, Osama Mohamed, Shaban, Mostafa, Al Thobaity, Abdulellah, Alkahtani, Saad Muaidh, El Arab, Rabie Adel·2024·DIGITAL HEALTH

Abstract

Introduction: Worldwide, healthcare systems aim to achieve the best possible quality of care at an affordable cost while ensuring broad access for all populations. The use of artificial intelligence (AI) in healthcare holds promise to address these challenges through the integration of real-world data-driven insights into patient care processes. This study aims to assess nurses’ awareness and attitudes toward AI-integrated tools used in clinical practice. Methods: A descriptive cross-sectional design captured nurses’ responses at three governmental hospitals in Saudi Arabia by using an online questionnaire administered over 4 months. The study involved 220 registered nurses with a minimum of one year of clinical experience, selected through a convenience sampling method. The online survey consisted of three sections: demographic information, an assessment of nurses’ AI knowledge, and the general attitudes toward the AI scale. Results: Nurses displayed “moderate” levels of awareness toward AI technology, with 70.9% having basic information about AI and only 58.2% (128 nurses) were considered “aware” of AI as they dealt with one of its healthcare applications. Nurses expressed openness to AI integration ( M = 3.51) on one side, but also had some concerns about AI. Nurses expressed conservative attitudes toward AI, with significant differences observed based on gender (χ² = 4.67, p < 0.05). Female nurses exhibited a higher proportion of negative attitudes compared to male nurses. Significant differences were also found based on age (χ² = 9.31, p < 0.05), with younger nurses demonstrating more positive attitudes toward AI compared to their older counterparts. Educational background yields significant differences (χ² = 6.70, p < 0.05), with nurses holding undergraduate degrees exhibiting the highest positive attitudes. However, years of nursing experience did not reveal significant variations in attitudes. Conclusion: Healthcare and nursing administrators need to work on increasing the nurses’ awareness of AI applications and emphasize the importance of integrating such technology into the systems in use. Moreover, addressing nurses’ concerns about AI's control and discomfort is crucial, especially considering generational differences, with younger nurses often having more positive attitudes toward technology. Change management strategies may help overcome any hindrances.

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

This cross-sectional survey of 220 Saudi registered nurses found moderate AI awareness and generally conservative attitudes toward AI in clinical practice. Demographic factors—gender, age, and education—were significantly associated with attitudes, while years of nursing experience were not.

Key findings

  • Awareness was moderate: 70.9% reported basic information about AI, 67.3% had heard of AI in medicine, and 58.2% (128 nurses) reported encountering/using AI means in the medical field.
  • Nurses showed some openness to AI, including interest in using AI in daily life (M = 3.51) and willingness to use AI in their job (M = 3.50), but also concerns about AI control (M = 3.15), personal consequences (M = 3.10), errors (M = 3.04), discomfort about future uses (M = 3.02), and danger (M = 2.93).
  • Significant attitude differences were found by gender (χ² = 4.67, p < 0.05), with more female nurses expressing negative attitudes (41.4%) than male nurses (10.5%); by age (χ² = 9.31, p < 0.05), with 20–29-year-olds most positive (20.5%) and 50–59-year-olds least positive (2.7%); and by education (χ² = 6.70, p < 0.05), with undergraduate-degree nurses most positive (32.3%), followed by postgraduate (17.4%) and diploma nurses (5%).
  • Years of nursing experience showed no significant variation in attitudes (χ² = 2.67, p = 0.61).

Methods

  • Descriptive cross-sectional online survey conducted in three governmental hospitals in the Jouf region, northern Saudi Arabia, over four months (April–September 2023).
  • Convenience sample of 220 registered nurses with at least one year of clinical experience; about 350 nurses were invited, yielding a 62.9% response rate.
  • Instrument included demographics, a 7-item “Yes/No” Nurses’ AI knowledge questionnaire (Cronbach’s alpha 0.795), and the 20-item General Attitudes toward AI Scale (GAAIS; 5-point Likert; positive subscale alpha 0.88, negative subscale alpha 0.82). Analysis used descriptive statistics and chi-squared tests.

Limitations

  • The paper states that generalization is limited because the study was restricted to nursing professionals in a specific setting and used a cross-sectional design; it recommends longitudinal and qualitative studies including nurses and other healthcare workers.
  • Evident from the text: convenience sampling, self-report online survey, and recruitment from only three governmental hospitals in one northern Saudi region limit generalizability.
  • Evident from the text: the sample was predominantly female (73.6%), and AI awareness was assessed largely through “Yes/No” items, which may not capture depth of knowledge.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1177/20552076241271803

Cites

27 of 27 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Verification

Claims the paper makes

28 Own result · 16 Not checked yet · 4 Own method · 2 Own interpretation

Verify all 24 references — free account

Show all 33 claims

  • It may state two separate claims as one. 12
  • The quote only partly states this claim. 1
  • Probably not a substantial claim. 6
17 of 17
  • The paper relies on this without support.

    The use of AI is not yet well understood among Saudi Arabian nurses, and little is known about their readiness, confidence, attitudes, and barriers to embracing AI in the clinical setting.

    “The use of AI is not yet well understood among Saudi Arabian nurses, as little is known about their readiness, confidence, attitudes, and barriers to embracing AI in the clinical setting.”
  • The paper relies on this without support.

    Nursing professionals may lack the knowledge, skills, and confidence to see the utilization of AI and digital health approaches, which explains why these resources aren’t utilized, or viewed as utilized, widely.

    “Nursing professionals may lack the knowledge, skills, and confidence to see the utilization of AI and digital health approaches, which explains why these resources aren’t utilized, or viewed as utilized, widely.”
  • The paper relies on this without support.

    Nurses are the end users of AI technology and interact directly with patients.

    “Because nurses are the end users of the technology and interact directly with patients, it is critical to assess their current understanding of AI to determine future training needs. 21”
  • The paper relies on this without support.

    AI and other technology applications are becoming part of nurses' daily work.

    “Nurses need to be introduced to the imminent transformation of nursing practice as AI and other technology applications are becoming part of their daily work for nurses.”
  • The paper relies on this without support.

    Nurses' roles and functions are being assisted by the different applications of technology including AI.

    “Nurses’ roles and functions are being assisted by the different applications of technology including AI.”
5 of 5

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Citations

Cites

1 retracted

Show all 27 references

Research works printed but not linked: 3

  1. WHO, 2016, Working for health and growth: Investing in the health workforce
  2. Nagle LM, 2020, Qual Adv-Avancées en formation infirmière, 6, 4
  3. Alhashmi S, 2019, Int J Inf Technol Lang Stud, 3, 27

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Metadata

Authors
Alruwaili, M. M., Abuadas, F. H., Alsadi, M., Alruwaili, A. N., Elsayed Ramadan, O. M., Shaban, M., Al Thobaity, A., Alkahtani, S. M., & El Arab, R. A.
Affiliations
  • Jouf University (SA)
  • Department of Maternity and Pediatric Health Nursing, College of Nursing, Jouf University, Sakaka, Saudi Arabia
  • Department of Medical Surgical Nursing, Nursing College, Taif University, Taif, Saudi Arabia
  • Ministry of Health (SA)
  • Department of Health Management and Informatics, AL Moosa College of Health Sciences, Al Ahsa, Saudi Arabia
Corresponding author
Abuadas, Fuad H.
Venue
DIGITAL HEALTH
Published
2024-01
Volume
10
Pages
20552076241271803-20552076241271803
ISSN
2055-2076 (ISSN-L)
OpenAlex
W4401376712
Cited by
114
References
27
Citations by year
2026: 59 · 2025: 47 · 2024: 8
FWCI
10.68
Type
journal article
Language
en
Open access
Locations
  • Digital Health (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Al Jouf University (Deanship of Scientific Research at Jouf University)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
  • Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
Keywords
artificial intelligence in healthcare, nurses' attitudes, nurses' awareness, AI knowledge, gender differences, age differences, educational attainment, generational differences, clinical implementation of AI, change management, nursing workforce

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