Release Date: 2023-09-14

The Use of Artificial Intelligence in the Diagnosis and Treatment of Diabetes

Release Date: 2023-09-14

Artificial intelligence (AI) is increasingly transforming the landscape of diabetes diagnosis and treatment by leveraging data-driven approaches to enhance precision and efficiency in healthcare. AI algorithms analyze vast amounts of patient data, including medical records, genetic profiles, and real-time physiological metrics from wearable devices, to identify patterns and predict disease progression. In diagnostics, AI-powered systems [...]

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    Work TypeBook Chapter
    Published inCurrent Multidisciplinary Approach to Diabetes Mellitus Occurrence Mechanism
    First Page161
    Last Page168
    DOIhttps://doi.org/10.69860/nobel.9786053359104.15
    LanguageENG
    Page Count8
    Copyright HolderNobel Tıp Kitabevleri
    Licensehttps://nobelpub.com/publish-with-us/copyright-and-licensing
    Artificial intelligence (AI) is increasingly transforming the landscape of diabetes diagnosis and treatment by leveraging data-driven approaches to enhance precision and efficiency in healthcare. AI algorithms analyze vast amounts of patient data, including medical records, genetic profiles, and real-time physiological metrics from wearable devices, to identify patterns and predict disease progression. In diagnostics, AI-powered systems can interpret complex datasets to facilitate early detection of diabetes and its complications, such as diabetic retinopathy and nephropathy, improving clinical outcomes through timely intervention. Furthermore, AI algorithms aid in personalized treatment strategies by optimizing insulin dosing regimens based on individual patient characteristics and response patterns. Machine learning models continue to evolve, offering healthcare providers decision support tools that streamline care delivery, enhance patient monitoring, and tailor therapeutic interventions to achieve better glycemic control and mitigate long-term complications of diabetes mellitus. As AI technologies advance, their integration into clinical practice holds promise for revolutionizing diabetes management, fostering proactive healthcare strategies, and ultimately improving patient outcomes.
    • International Diabetes Federation. IDF Diabetes Atlas, 10th edn. Brussels, Belgium: 2021. Available at: https://www.diabetesatlas.org

    • Fitzmaurice C, Allen C, Barber RM, et al. Global, regional, and national cancer incidence, mortality, years of life lost, years lived with disability, and disability-adjusted life-years for 32 cancer groups, 1990 to 2015: a systematic analysis for the global burden of disease study. JAMA oncology; 2017, 3(4), 524-548.

    • Ellahham S. Artifi cial intelligence: the future for diabetes care. The American journal of medicine; 2020, 133(8), 895-900.

    • Osmanoglu UO. Prediction of heart failure mortality by machine learning classifi cation algorithms. Ph.D. thesis, Eskisehir Osmangazi University; 2021.

    • Nimri R, Battelino T, Laffel LM, et al. Insulin dose optimization using an automated artifi cial intelligencebased decision support system in youths with type 1 diabetes. Nature medicine; 2020, 26(9), 1380-1384.

    • Abbasi F, Saba S, Ebrahim-Habibi A, et al. Detection of KCNJ11 gene mutations in a family with neonatal diabetes mellitus: implications for therapeutic management of family members with long-standing disease. Molecular diagnosis & therapy; 2012, 16, 109-114.

    • Choi BG, Rha SW, Kim SW, et al. Machine learning for the prediction of new-onset diabetes mellitus during 5-year follow-up in non-diabetic patients with cardiovascular risks. Yonsei Med J; 2019, 60(2), 191–9.

    • Kopitar L, Kocbek P, Cilar L, et al. Early detection of type 2 diabetes mellitus using machine learningbased prediction models. Sci Rep; 2020, 10(1), 11981.

    • Xiong Y, Lin L, Chen Y, et al. Prediction of gestational diabetes mellitus in the fi rst 19 weeks of pregnancy using machine learning techniques. The journal of maternal-fetal & neonatal medicine; 2022, 35(13), 2457- 2463.

    • Er MB, İbrahim I. LSTM tabanlı derin ağlar kullanılarak diyabet hastalığı tahmini. Türk Doğa ve Fen Dergisi; 2021, 10(1), 68-74.

    • Ganie SM, Malik MB, Arif T. Early prediction of diabetes mellitus using various artifi cial intelligence techniques: a technological review. International Journal of Business Intelligence and Systems Engineering; 2021, 1(4), 325-346.

    • Pei X, Yao X, Yang Y, et al. Effi cacy of artifi cial intelligence-based screening for diabetic retinopathy in type 2 diabetes mellitus patients. Diabetes Research and Clinical Practice; 2022, 184, 109190.

    • Zhang J, Wang F. Prediction of Gestational Diabetes Mellitus under Cascade and Ensemble Learning Algorithm. Computational Intelligence and Neuroscience; 2022.

    • Chou CY, Hsu DY, Chou CH. Predicting the Onset of Diabetes with Machine Learning Methods. Journal of Personalized Medicine; 2023, 13(3), 406.

    • Khaleel FA, Al-Bakry AM. Diagnosis of diabetes using machine learning algorithms. Materials Today: Proceedings; 2023, 80, 3200-3203.

    • Sonia JJ, Jayachandran P, Md AQ, et al. Machine-Learning-Based Diabetes Mellitus Risk Prediction Using Multi-Layer Neural Network No-Prop Algorithm. Diagnostics; 2023, 13(4), 723.

    • International Diabetes Federation. IDF Diabetes Atlas, 7th edn. Brussels, Belgium: 2015. Available at: https://www.diabetesatlas.org

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