ARTIFICIAL INTELLIGENCE IN TREATMENT OF DIABETES: ENHANCING ACCURACY AND PREDICTIVE OUTCOMES
Abstract
Diabetes mellitus is a chronic metabolic disorder with rising global prevalence, often complicated by cardiovascular, renal, and neurological conditions. Conventional management relies on intermittent glucose monitoring and standardized therapeutic regimens, which frequently fail to provide personalized, predictive, or timely interventions. Artificial Intelligence (AI), including machine learning and deep learning algorithms, has shown promise in improving diagnosis, monitoring, predictive outcomes, and individualized treatment. This thesis examines the application of AI in diabetes care, highlighting its role in enhancing accuracy, forecasting glycemic trends, and facilitating patient-centered management. Ethical and clinical implementation considerations are also discussed.
References
American Diabetes Association. (2022) Standards of Medical Care in Diabetes—2022. Diabetes Care, 45(Suppl. 1), pp. S1–S264.
Bertalan, M. et al. (2021) ‘Artificial Intelligence in Diabetes Management: Remote Monitoring and Telemedicine Applications,’ Journal of Diabetes Science and Technology, 15(4), pp. 752–762.
Char, D.S., Shah, N.H. and Magnus, D. (2018) ‘Implementing Machine Learning in Health Care — Addressing Ethical Challenges,’ New England Journal of Medicine, 378(11), pp. 981–983.
Contreras, I. and Vehi, J. (2018) ‘Artificial Intelligence for Diabetes Management and Decision Support: Literature Review,’ Journal of Medical Internet Research, 20(5), e10775.
Gulshan, V. et al. (2016) ‘Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs,’ JAMA, 316(22), pp. 2402–2410.
Heinemann, L., Freckmann, G., Ehrmann, D. et al. (2018) ‘Real-time Continuous Glucose Monitoring in Adults With Diabetes: Accuracy and Clinical Use,’ Diabetes Technology & Therapeutics, 20(S1), pp. S1–S15.
Hirsch, I.B., Brownlee, M., Bergenstal, R.M. et al. (2020) ‘Artificial Intelligence and Reinforcement Learning for Insulin Therapy in Type 1 Diabetes,’ Diabetes Care, 43(10), pp. 2341–2349.
IDF (2019) IDF Diabetes Atlas, 9th edition. Brussels: International Diabetes Federation.
Khunti, K., Gomes, M.B., Pocock, S. et al. (2018) ‘Therapeutic Inertia in Type 2 Diabetes: Prevalence, Causes, and Solutions,’ Diabetes Obesity and Metabolism, 20(5), pp. 962–972.
Klonoff, D.C. (2019) ‘Continuous Glucose Monitoring: Roadmap for 21st Century Diabetes Management,’ Diabetes Care, 42(9), pp. 1702–1711.
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Copyright (c) 2025 Fazliddin Arziqulov, Sayfullayeva Dilbar Izzatillayevna, Maxsudov Valijon Gafurjonovich (Author)
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