Artificial Intelligence for Diabetes Management and Decision Support: Literature Review

Overview

Journal J Med Internet Res

Publisher JMIR Publications

Specialty Medical Informatics

Date 2018 Jun 1

PMID 29848472

Citations 128

Authors

Ivan Contreras

Josep Vehi

Affiliations

Soon will be listed here.

Abstract

Background: Artificial intelligence methods in combination with the latest technologies, including medical devices, mobile computing, and sensor technologies, have the potential to enable the creation and delivery of better management services to deal with chronic diseases. One of the most lethal and prevalent chronic diseases is diabetes mellitus, which is characterized by dysfunction of glucose homeostasis.

Objective: The objective of this paper is to review recent efforts to use artificial intelligence techniques to assist in the management of diabetes, along with the associated challenges.

Methods: A review of the literature was conducted using PubMed and related bibliographic resources. Analyses of the literature from 2010 to 2018 yielded 1849 pertinent articles, of which we selected 141 for detailed review.

Results: We propose a functional taxonomy for diabetes management and artificial intelligence. Additionally, a detailed analysis of each subject category was performed using related key outcomes. This approach revealed that the experiments and studies reviewed yielded encouraging results.

Conclusions: We obtained evidence of an acceleration of research activity aimed at developing artificial intelligence-powered tools for prediction and prevention of complications associated with diabetes. Our results indicate that artificial intelligence methods are being progressively established as suitable for use in clinical daily practice, as well as for the self-management of diabetes. Consequently, these methods provide powerful tools for improving patients' quality of life.

Citing Articles

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Boccardi V, Bahat G, Balci C, Bourdel-Marchasson I, Christiaens A, Donini L Eur Geriatr Med. 2025; .

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Enhancing communication skills in diabetes care: an observational study regarding the impact of role-playing training for medical staff.

Kishimoto M, Kaneko Y, Wakui-Kimura A, Tachibana Y, Sato F, Kikuchi T BMC Med Educ. 2025; 25(1):293.

PMID: 39987053 PMC: 11847350. DOI: 10.1186/s12909-025-06821-8.

Digital health technologies and innovation patterns in diabetes ecosystems.

Giger O, Pfitzer E, Mekniran W, Gebhardt H, Fleisch E, Jovanova M Digit Health. 2025; 11:20552076241311740.

PMID: 39911718 PMC: 11795620. DOI: 10.1177/20552076241311740.

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Selim R, Basu A, Anto A, Foscht T, Eisingerich A JMIR Form Res. 2024; 8():e64081.

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Beyond Expected Patterns in Insulin Needs of People With Type 1 Diabetes: Temporal Analysis of Automated Insulin Delivery Data.

Degen I, Robson Brown K, Reeve H, Abdallah Z JMIRx Med. 2024; 5:e44384.

PMID: 39654139 PMC: 11612581. DOI: 10.2196/44384.

References

Rhyner D, Loher H, Dehais J, Anthimopoulos M, Shevchik S, Botwey R . Carbohydrate Estimation by a Mobile Phone-Based System Versus Self-Estimations of Individuals With Type 1 Diabetes Mellitus: A Comparative Study. J Med Internet Res. 2016; 18(5):e101. PMC: 4880742. DOI: 10.2196/jmir.5567. View

Frandes M, Timar B, Timar R, Lungeanu D . Chaotic time series prediction for glucose dynamics in type 1 diabetes mellitus using regime-switching models. Sci Rep. 2017; 7(1):6232. PMC: 5524948. DOI: 10.1038/s41598-017-06478-4. View

Miller S, Nimri R, Atlas E, Grunberg E, Phillip M . Automatic learning algorithm for the MD-logic artificial pancreas system. Diabetes Technol Ther. 2011; 13(10):983-90. DOI: 10.1089/dia.2010.0216. View

Herrero P, Bondia J, Adewuyi O, Pesl P, El-Sharkawy M, Reddy M . Enhancing automatic closed-loop glucose control in type 1 diabetes with an adaptive meal bolus calculator - in silico evaluation under intra-day variability. Comput Methods Programs Biomed. 2017; 146:125-131. PMC: 6522376. DOI: 10.1016/j.cmpb.2017.05.010. View

Yom-Tov E, Feraru G, Kozdoba M, Mannor S, Tennenholtz M, Hochberg I . Encouraging Physical Activity in Patients With Diabetes: Intervention Using a Reinforcement Learning System. J Med Internet Res. 2017; 19(10):e338. PMC: 5654735. DOI: 10.2196/jmir.7994. View

Heringa M, Siderius H, Floor-Schreudering A, De Smet P, Bouvy M . Lower alert rates by clustering of related drug interaction alerts. J Am Med Inform Assoc. 2016; 24(1):54-59. PMC: 7654078. DOI: 10.1093/jamia/ocw049. View

Brown D, Aldea A, Harrison R, Martin C, Bayley I . Temporal case-based reasoning for type 1 diabetes mellitus bolus insulin decision support. Artif Intell Med. 2017; 85:28-42. DOI: 10.1016/j.artmed.2017.09.007. View

Fico G, Fioravanti A, Arredondo M, Ardigo D, Guillen A . A healthy lifestyle coaching-persuasive application for patients with type 2 diabetes. Annu Int Conf IEEE Eng Med Biol Soc. 2010; 2010:2221-4. DOI: 10.1109/IEMBS.2010.5626185. View

Cleveringa F, Minkman M, Gorter K, van den Donk M, Rutten G . Diabetes Care Protocol: effects on patient-important outcomes. A cluster randomized, non-inferiority trial in primary care. Diabet Med. 2010; 27(4):442-50. DOI: 10.1111/j.1464-5491.2010.02968.x. View

10.

Caballero-Ruiz E, Garcia-Saez G, Rigla M, Villaplana M, Pons B, Elena Hernando M . A web-based clinical decision support system for gestational diabetes: Automatic diet prescription and detection of insulin needs. Int J Med Inform. 2017; 102:35-49. DOI: 10.1016/j.ijmedinf.2017.02.014. View

11.

Zarkogianni K, Vazeou A, Mougiakakou S, Prountzou A, Nikita K . An insulin infusion advisory system based on autotuning nonlinear model-predictive control. IEEE Trans Biomed Eng. 2011; 58(9):2467-77. DOI: 10.1109/TBME.2011.2157823. View

12.

Herrero P, Pesl P, Reddy M, Oliver N, Georgiou P, Toumazou C . Advanced Insulin Bolus Advisor Based on Run-To-Run Control and Case-Based Reasoning. IEEE J Biomed Health Inform. 2014; 19(3):1087-96. DOI: 10.1109/JBHI.2014.2331896. View

13.

Pappada S, Cameron B, Rosman P, Bourey R, Papadimos T, Olorunto W . Neural network-based real-time prediction of glucose in patients with insulin-dependent diabetes. Diabetes Technol Ther. 2011; 13(2):135-41. DOI: 10.1089/dia.2010.0104. View

14.

Fico G, Fioravanti A, Arredondo M, Leuteritz J, Guillen A, Fernandez D . A user centered design approach for patient interfaces to a diabetes IT platform. Annu Int Conf IEEE Eng Med Biol Soc. 2012; 2011:1169-72. DOI: 10.1109/IEMBS.2011.6090274. View

15.

Ewings S, Sahu S, Valletta J, Byrne C, Chipperfield A . A Bayesian network for modelling blood glucose concentration and exercise in type 1 diabetes. Stat Methods Med Res. 2014; 24(3):342-72. DOI: 10.1177/0962280214520732. View

16.

McCoy R, Ngufor C, Van Houten H, Caffo B, Shah N . Trajectories of Glycemic Change in a National Cohort of Adults With Previously Controlled Type 2 Diabetes. Med Care. 2017; 55(11):956-964. PMC: 5640500. DOI: 10.1097/MLR.0000000000000807. View

17.

Rigla M, Garcia-Saez G, Pons B, Hernando M . Artificial Intelligence Methodologies and Their Application to Diabetes. J Diabetes Sci Technol. 2017; 12(2):303-310. PMC: 5851211. DOI: 10.1177/1932296817710475. View

18.

Valdez R, Guterbock T, Fitzgibbon K, Williams I, Wellbeloved-Stone C, Bears J . From loquacious to reticent: understanding patient health information communication to guide consumer health IT design. J Am Med Inform Assoc. 2017; 24(4):680-696. PMC: 7651950. DOI: 10.1093/jamia/ocw155. View

19.

Samadi S, Rashid M, Turksoy K, Feng J, Hajizadeh I, Hobbs N . Automatic Detection and Estimation of Unannounced Meals for Multivariable Artificial Pancreas System. Diabetes Technol Ther. 2018; 20(3):235-246. PMC: 5867514. DOI: 10.1089/dia.2017.0364. View

20.

Daskalaki E, Prountzou A, Diem P, Mougiakakou S . Real-time adaptive models for the personalized prediction of glycemic profile in type 1 diabetes patients. Diabetes Technol Ther. 2011; 14(2):168-74. DOI: 10.1089/dia.2011.0093. View