Volume 30, Issue 4 (Autumn 2024)
Intern Med Today 2024, 30(4): 0-0 |
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Kashani M, Barzekar S. A Systematic Review of Artificial Intelligence Applications for Education and Support of Diabetic Patients in Wartime and Crisis Settings. Intern Med Today 2024; 30 (4)
URL: http://imtj.gmu.ac.ir/article-1-4107-en.html
URL: http://imtj.gmu.ac.ir/article-1-4107-en.html
1- Department of Health Information Technology, Sirjan School of Medical Sciences, Sirjan, Iran. , mostafa.kashani@sirums.ac.ir
2- Sirjan School of Medical Sciences, Sirjan, Iran.
2- Sirjan School of Medical Sciences, Sirjan, Iran.
Abstract: (15 Views)
Background: Diabetes management faces critical disruptions during wartime, including medical shortages and infrastructure collapse. This systematic review evaluates the role of Artificial Intelligence (AI) in providing education and support for diabetic patients in conflict settings.
Methods: In accordance with PRISMA guidelines, a targeted search was conducted in PubMed, Scopus, IEEE Xplore, and Web of Science (2015–2023). Using PICO criteria, 20 high-quality studies were selected from 1,523 records. Methodological quality was assessed using AMSTAR-2, RoB 2, and NOS tools.
Results: Findings indicate that AI improves glucose prediction accuracy to 97%, reduces HbA1c by an average of 1.5%, and lowers the risk of hypoglycemia by 31%. AI-driven educational tools, including interactive chatbots and digital platforms, enhanced patient awareness and treatment adherence by 25% in wartime conditions. Key applications identified include glucose monitoring, predictive analytics, digital education, and psychological support. However, challenges such as limited real-world wartime data and ethical concerns regarding data privacy remain significant.
Conclusion: AI significantly transforms diabetes care and self-education during humanitarian crises. Although current models show high efficacy in clinical simulations, future research must focus on developing robust, ethical, and inclusive AI frameworks, specifically validated for active conflict zones, to ensure equitable care delivery.
Methods: In accordance with PRISMA guidelines, a targeted search was conducted in PubMed, Scopus, IEEE Xplore, and Web of Science (2015–2023). Using PICO criteria, 20 high-quality studies were selected from 1,523 records. Methodological quality was assessed using AMSTAR-2, RoB 2, and NOS tools.
Results: Findings indicate that AI improves glucose prediction accuracy to 97%, reduces HbA1c by an average of 1.5%, and lowers the risk of hypoglycemia by 31%. AI-driven educational tools, including interactive chatbots and digital platforms, enhanced patient awareness and treatment adherence by 25% in wartime conditions. Key applications identified include glucose monitoring, predictive analytics, digital education, and psychological support. However, challenges such as limited real-world wartime data and ethical concerns regarding data privacy remain significant.
Conclusion: AI significantly transforms diabetes care and self-education during humanitarian crises. Although current models show high efficacy in clinical simulations, future research must focus on developing robust, ethical, and inclusive AI frameworks, specifically validated for active conflict zones, to ensure equitable care delivery.
Type of Study: Review |
Subject:
Basic Medical Science
Received: 2025/09/23 | Accepted: 2026/04/26 | Published: 2024/09/29
Received: 2025/09/23 | Accepted: 2026/04/26 | Published: 2024/09/29
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