Artificial intelligence in type 1 diabetes mellitus
DOI:
https://doi.org/10.12775/JEHS.2025.79.57913Keywords
diabetes mellitus, artificial intelligence, endocrinologyAbstract
Type I diabetes is an autoimmune disease in the course of which insulin levels are reduced and hyperglycemia occurs. Treatment options for type I diabetes have changed a lot over time. A large contribution to advances in the field of diabetes treatment has been made by artificial intelligence. Originally, the treatment consisted of multiple finger punctures per day and multiple insulin injections. But now, thanks to artificial intelligence technology, a number of solutions are available including continuous glucose monitors and, based on these, a decision support system. This makes it possible to reduce the number of finger pricks and the frequency of insulin administration. Above that, it makes it possible to tailor the treatment process to the patient, prepare personalized recommendations and respond quickly to changes in serum glucose levels.
References
1. Chaki J, Thillai Ganesh S, Cidham SK, Ananda Theertan S. Machine learning and artificial intelligence based Diabetes Mellitus detection and self-management: A systematic review. Journal of King Saud University - Computer and Information Sciences. 2022;34(6, Part B):3204-25.
2. Sharma N, Singh A, editors. Diabetes detection and prediction using machine learning/IoT: A survey. Advanced Informatics for Computing Research: Second International Conference, ICAICR 2018, Shimla, India, July 14–15, 2018, Revised Selected Papers, Part I 2; 2019: Springer.
3. Drake R, Vogl AW, Mitchell AW. Gray's anatomy for students E-book: Elsevier Health Sciences; 2009.
4. Wilkins LW, King J. Anatomy and Physiology: Lippincott Williams & Wilkins; 2002.
5. Katsarou A, Gudbjörnsdottir S, Rawshani A, Dabelea D, Bonifacio E, Anderson BJ, et al. Type 1 diabetes mellitus. Nat Rev Dis Primers. 2017;3:17016.
6. Vonasek J, Larsen IM, Nikontovic A, Thorvig CM. A Novel Follow‐Up Model for Type 1 Diabetes in Children Leads to Higher Glycemic Control. Pediatric Diabetes. 2025;2025(1):6920068.
7. Mondal S, Pappachan JM. Current perspectives and the future of disease-modifying therapies in type 1 diabetes. World J Diabetes. 2025;16(1):99496.
8. Insel RA, Dunne JL, Atkinson MA, Chiang JL, Dabelea D, Gottlieb PA, et al. Staging presymptomatic type 1 diabetes: a scientific statement of JDRF, the Endocrine Society, and the American Diabetes Association. Diabetes Care. 2015;38(10):1964-74.
9. Federation ID. 2015.
10. DeFronzo RA, Ferrannini E, Groop L, Henry RR, Herman WH, Holst JJ, et al. Type 2 diabetes mellitus. Nat Rev Dis Primers. 2015;1:15019.
11. Diaz-Valencia PA, Bougneres P, Valleron AJ. Global epidemiology of type 1 diabetes in young adults and adults: a systematic review. BMC Public Health. 2015;15:255.
12. Shapiro SC, editor Encyclopedia of artificial intelligence, vols. 1 and 2 (2nd ed.)1992.
13. Holzinger A, Langs G, Denk H, Zatloukal K, Müller H. Causability and explainability of artificial intelligence in medicine. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 2019;9(4):e1312.
14. Ramesh AN, Kambhampati C, Monson JR, Drew PJ. Artificial intelligence in medicine. Ann R Coll Surg Engl. 2004;86(5):334-8.
15. Steimann F. On the use and usefulness of fuzzy sets in medical AI. Artif Intell Med. 2001;21(1-3):131-7.
16. Naskar S, Sharma S, Kuotsu K, Halder S, Pal G, Saha S, et al. The Biomedical Applications of Artificial Intelligence: An Overview of Decades of Research. Journal of Drug Targeting. 2025(just-accepted):1-85.
17. Alkalifah B, Shaheen MT, Alotibi J, Alsubait T, Alhakami H. Evaluation of machine learning-based regression techniques for prediction of diabetes levels fluctuations. Heliyon. 2025;11(1).
18. Foster NC, Beck RW, Miller KM, Clements MA, Rickels MR, DiMeglio LA, et al. State of Type 1 Diabetes Management and Outcomes from the T1D Exchange in 2016-2018. Diabetes Technol Ther. 2019;21(2):66-72.
19. Martens T, Beck RW, Bailey R, Ruedy KJ, Calhoun P, Peters AL, et al. Effect of Continuous Glucose Monitoring on Glycemic Control in Patients With Type 2 Diabetes Treated With Basal Insulin: A Randomized Clinical Trial. Jama. 2021;325(22):2262-72.
20. Olczuk D, Priefer R. A history of continuous glucose monitors (CGMs) in self-monitoring of diabetes mellitus. Diabetes Metab Syndr. 2018;12(2):181-7.
21. Phillip M, Danne T, Shalitin S, Buckingham B, Laffel L, Tamborlane W, et al. Use of continuous glucose monitoring in children and adolescents. Pediatric diabetes. 2012;13(3):215-28.
22. Charpentier G, Benhamou PY, Dardari D, Clergeot A, Franc S, Schaepelynck-Belicar P, et al. The Diabeo software enabling individualized insulin dose adjustments combined with telemedicine support improves HbA1c in poorly controlled type 1 diabetic patients: a 6-month, randomized, open-label, parallel-group, multicenter trial (TeleDiab 1 Study). Diabetes Care. 2011;34(3):533-9.
23. Kirwan M, Vandelanotte C, Fenning A, Duncan MJ. Diabetes self-management smartphone application for adults with type 1 diabetes: randomized controlled trial. J Med Internet Res. 2013;15(11):e235.
24. Drion I, Pameijer LR, van Dijk PR, Groenier KH, Kleefstra N, Bilo HJ. The Effects of a Mobile Phone Application on Quality of Life in Patients With Type 1 Diabetes Mellitus: A Randomized Controlled Trial. J Diabetes Sci Technol. 2015;9(5):1086-91.
25. Skrøvseth SO, Årsand E, Godtliebsen F, Joakimsen RM. Data-Driven Personalized Feedback to Patients with Type 1 Diabetes: A Randomized Trial. Diabetes Technol Ther. 2015;17(7):482-9.
26. Senseonic. Senseonics announces fda approval of the eversense e3 continuous glucose monitoring system for use for up to 6 months 2022 [Available from: https: // www. senseonics. com/ investor-relations/ news-releases/ 2022/ 02-11-2022-120033959
27. Tyler NS, Jacobs PG. Artificial Intelligence in Decision Support Systems for Type 1 Diabetes. Sensors (Basel). 2020;20(11).
28. Veazie S, Winchell K, Gilbert J, Paynter R, Ivlev I, Eden KB, et al. Rapid Evidence Review of Mobile Applications for Self-management of Diabetes. J Gen Intern Med. 2018;33(7):1167-76.
29. Wu Y, Yao X, Vespasiani G, Nicolucci A, Dong Y, Kwong J, et al. Mobile App-Based Interventions to Support Diabetes Self-Management: A Systematic Review of Randomized Controlled Trials to Identify Functions Associated with Glycemic Efficacy. JMIR Mhealth Uhealth. 2017;5(3):e35.
30. D’Antoni F. Artificial Intelligence Models for the Management of Type 1 Diabetes. 2023.
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Copyright (c) 2025 Wiktoria Łoskot, Jan Szwech, Mateusz Matczak, Karol Jasiński, Aleksandra Broda, Kacper Hoksa, Krzysztof Jodłowski, Ewa Dubniewicz, Paula Majewska, Alicja Staszek
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