Omar Qaladi, Mukhlid Alshammari, Abdullah Abdulrahim Almalki
Abstract
Artificial Intelligence (AI) is increasingly transforming nursing administration by enhancing operational efficiency and supporting data-driven decision-making. This study explores registered nurses perceptions of AI in Saudi Arabia, focusing on both challenges and opportunities.
Introduction
Technological advancements in AI are transforming healthcare by improving diagnostic accuracy, decision-making, and administrative efficiency [1,2]. Nursing benefits from AI’s potential to optimize patient care and streamline operations, yet requires new competencies in technology and data use [3,4].
Methods
This research adopted a cross-sectional design to capture a snapshot of nurses’ perceptions of AI integration in nursing administration and practice. The cross-sectional approach facilitated the collection of data at a single point in time, offering insights into the prevailing attitudes and characteristics of the study population [14].
Results
A total of 202 nurses participated in the study as shown in Table 1. The majority (44.6%) of respondents are aged between 23-26 age group, with a higher representation of females (61.9%).
Discussion
The study reveals nurses’ nuanced perceptions of AI, recognizing both its benefits and challenges. The high familiarity with AI tools among participants suggests increasing integration of AI in nursing, aligning with studies from Egypt and Germany that indicate positive trends in AI acceptance among nurses [10,16].
Conclusion
This study shows that nurses generally view AI positively, recognizing its potential to enhance education, improve efficiency, and aid in diagnosis. However, concerns remain around AI’s impact on critical thinking, emotional assessment, job security, and ethical issues.
Citation: Qaladi O, Alshammari M, Abdulrahim Almalki A (2025) Artificial intelligence (AI) in nursing administration: Challenges and opportunities. PLoS ONE 20(4): e0319588. https://doi.org/10.1371/journal.pone.0319588
Editor: Majed Sulaiman Alamri, University of Hafr Al-Batin, SAUDI ARABIA
Received: September 16, 2024; Accepted: February 4, 2025; Published: April 1, 2025
Copyright: © 2025 Qaladi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The dataset underlying this study contains sensitive personal information, including data that could potentially identify participants. Public sharing of the data is restricted to protect participant privacy and confidentiality, in compliance with ethical guidelines established by the Taif health cluster Institutional Review Board (THC IRB). Access to the data can be requested from the THC IRB. Requests will be reviewed to ensure compliance with ethical and legal standards. Please contact the THC IRB via email at Thc-sr@moh.gov.sa for further details.
Funding: The author(s) received no specific funding for this work.
Competing interests: No authors have competing interests.
Abbreviations: AI, Artificial intelligence; KACST, King Abdulaziz City for Science and Technology; KASH, King Abdulazize Spcialist Hospital; KFMC, King Faisal Medical Complex.
