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1- Student Research Committee, Khoy University of Medical Sciences, Khoy, Iran
2- Department of Nursing and Midwifery, Khoy.C., Islamic Azad University, Khoy, Iran
3- Department of Health Information Technology, Urmia University of Medical Sciences, Urmia, Iran
4- Student Research Committee, Tabriz University of Medical Sciences, Tabriz, Iran ,amirhosseinseyednazari@gmail.com
2- Department of Nursing and Midwifery, Khoy.C., Islamic Azad University, Khoy, Iran
3- Department of Health Information Technology, Urmia University of Medical Sciences, Urmia, Iran
4- Student Research Committee, Tabriz University of Medical Sciences, Tabriz, Iran ,
Abstract: (44 Views)
Background and Aims: Integrating Artificial Intelligence (AI) into diabetes management offers proactive care potential' however, it faces resistance in developing countries. This study aimed to identify AI adoption barriers within emerging economies.
Materials and Methods: This systematic review was conducted based on the PRISMA protocol. A comprehensive search was conducted using PubMed, Scopus, Web of Science, and CINAHL data bases on January 10, 2026. The period from 2019 to 2025 was chosen to cover digital developments after the pandemic. Inclusion criteria were original articles (quantitative, qualitative, and mixed) that evaluated the barriers to the adoption of artificial intelligence by diabetes nurses. The quality of the articles was evaluated using the Mixed Methods Appraisal Tool (MMAT). The obtained data were then analyzed using the narrative synthesis approach.
Results: From a total of 1512 primary records, after careful screening, 10 original studies conducted in emerging economies were included in the final review. Data analysis revealed four main barriers: 1) Infrastructural: high internet costs and electricity instability; 2) Knowledge gap: significant difference between positive attitude and low literacy; 3) Psychological: fear of system error in critical situations; and 4) Demographics: the "paradox of experience" phenomenon (older nurses have a more positive attitude than younger ones).
Conclusion: Unlike developed nations focusing on privacy, emerging economies primarily struggle with infrastructure and foundational literacy. Policymakers should prioritize "low-tech" AI integration and practical training curricula.
Materials and Methods: This systematic review was conducted based on the PRISMA protocol. A comprehensive search was conducted using PubMed, Scopus, Web of Science, and CINAHL data bases on January 10, 2026. The period from 2019 to 2025 was chosen to cover digital developments after the pandemic. Inclusion criteria were original articles (quantitative, qualitative, and mixed) that evaluated the barriers to the adoption of artificial intelligence by diabetes nurses. The quality of the articles was evaluated using the Mixed Methods Appraisal Tool (MMAT). The obtained data were then analyzed using the narrative synthesis approach.
Results: From a total of 1512 primary records, after careful screening, 10 original studies conducted in emerging economies were included in the final review. Data analysis revealed four main barriers: 1) Infrastructural: high internet costs and electricity instability; 2) Knowledge gap: significant difference between positive attitude and low literacy; 3) Psychological: fear of system error in critical situations; and 4) Demographics: the "paradox of experience" phenomenon (older nurses have a more positive attitude than younger ones).
Conclusion: Unlike developed nations focusing on privacy, emerging economies primarily struggle with infrastructure and foundational literacy. Policymakers should prioritize "low-tech" AI integration and practical training curricula.
Type of Study: Review |
Subject:
Medical-Surgical Nursing
Received: 2026/01/7 | Accepted: 2026/04/20 | ePublished ahead of print: 2026/06/10
Received: 2026/01/7 | Accepted: 2026/04/20 | ePublished ahead of print: 2026/06/10
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