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Volume 13, Issue 4 (2025)
Health Educ Health Promot 2025, 13(4): 759-768 |
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Received: 2025/10/6 | Accepted: 2025/12/13 | Published: 2025/11/19
Received: 2025/10/6 | Accepted: 2025/12/13 | Published: 2025/11/19
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Esmaili dolabenezhad S, bahrambeygi F, besharati S. Artificial Intelligence Application in Nursing to Accelerate the Treatment Process of Iranian Patients. Health Educ Health Promot 2025; 13 (4) :759-768
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URL: http://hehp.daneshafarand.org/article-4-82927-en.html
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Abstract (542 Views)
Aims: The integration of artificial intelligence in healthcare represents a transformative shift in nursing practice and patient care delivery. This review article synthesized the current literature on the application of artificial intelligence in nursing practice and aimed to accelerate treatment processes for patients, with a focus on Iran.
Information & Methods: This systematic review analyzed multiple studies conducted between 2023 and 2025 on artificial intelligence integration in Iranian nursing practice published in databases, including PubMed, CINAHL, and Google Scholar, using keywords related to artificial intelligence, nursing, Iran, and patient treatment acceleration. Data from both qualitative and quantitative studies were synthesized to provide a comprehensive overview.
Findings: Iranian nurses demonstrated moderate acceptance (74.6% at moderate level) and positive attitudes (65.8% with good attitude) toward artificial intelligence despite significant knowledge gaps (41.1% with low knowledge). Artificial intelligence applications in Iranian healthcare settings have shown promise in predictive analytics, clinical decision support systems, and administrative task automation. Significant positive correlations were found between nurses’ knowledge and their attitude (R=0.311, p<0.001), application (R=0.514, p<0.001), and acceptance (R=0.381, p<0.001) of artificial intelligence technologies.
Conclusion: Artificial intelligence technologies hold significant promise for accelerating treatment processes in Iranian healthcare settings through enhanced decision-making, predictive analytics, and workflow automation.
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