Artificial intelligence is growing quickly, and its application in the global diabetes pandemic has the potential to completely change the way this chronic illness is identified and treated. Machine learning methods have been used to construct algorithms supporting predictive models for the risk of getting diabetes or its complications. Social media and Internet forums also increase patient participation in diabetes care. Diabetes resource usage optimisation has benefited from technological improvements. As a lifestyle therapy intervention, digital therapies have made a name for themselves in the treatment of diabetes. Artificial intelligence will cause a paradigm shift in diabetes care, moving away from current methods and toward the creation of focused, data-driven precision treatment.
Hruby A, Hu FB. The epidemiology of obesity: a big picture. Pharmacoeconomics 2015; 33: 673-89.
Jia W. Diabetes care in China: innovations and implications. J. Diabetes Investig 2002; 13: 1795-7.
The Prevention of Diabetes Mellitus. JAMA 2021; 325: 190.
ElSayed NA, Aleppo G, Aroda VR, et al. Facilitating positive health behaviors and well-being to improve health outcomes: standards of care in diabetes-2023. Diabetes Care 2023; 46: S68-96.
Jiang F, Jiang Y, Zhi H, et al. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol 2017; 2: 230-43.
Tran KA, Kondrashova O, Bradley A, Williams ED, Pearson JV, Waddell N. Deep learning in cancer diagnosis, prognosis and treatment selection. Genome Med 2021; 13: 152.
LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015; 521: 436-44.
Ashrafzadeh S, Hamdy O. Patient-driven diabetes care of the future in the technology era. Cell Metabol 2019; 29: 564-75.
Ramos JMA, Perdomo O, Gonzalez FA. Deep semi-supervised and self-supervised learning for diabetic retinopathy detection. 2022 Preprint at arXiv.
Deo RC. Machine learning in medicine. Circulation 2015; 132: 1920-30.
Yu KH, Snyder M. Omics profiling in precision oncology. Mol Cell Proteomics 2016; 15: 2525-36.
Ahlqvist E, Storm P, Karajamaki A, et al. Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables. Lancet Diabetes Endocrinol 2018; 6: 361-9.
Zou X, Zhou X, Zhu Z, Ji L. Novel subgroups of patients with adult-onset diabetes in Chinese and US populations. Lancet Diabetes Endocrinol 2019; 7: 9-11.
van Engelen JE, Hoos HH. A survey on semi-supervised learning. Mach Learn 2020; 109: 373-440.
Coronato A, Naeem M, De Pietro G, Paragliola G. Reinforcement learning for intelligent healthcare applications: a survey. Artif Intell Med 2020; 109: 101964.
Gottesman O, Johansson F, Komorowski M, et al. Guidelines for reinforcement learning in healthcare. Nat Med 2019; 25: 16-8.
Samuel AL. Some studies in machine learning using the game of checkers. IBM J 1967; 601-7.
Deberneh HM, Kim I. Prediction of type 2 diabetes based on machine learning algorithm. Int J Environ Res Public Health 2021; 18: 3317.
Dinh A, Miertschin S, Young A, Mohanty SD. A data-driven approach to predicting diabetes and cardiovascular disease with machine learning. BMC Med Inform Decis Mak 2019; 19: 211.
Zueger T, Schallmoser S, Kraus M, Saar-sechansky M, Feuer Riegel S, Stettler C. Machine learning for predicting the risk of transition from prediabetes to diabetes. Diabetes Technol Ther 2022; 24: 842-7.
Baig MM, Hosseini HG, Ullah JGE, Lindén M. Early detection of prediabetes and T2DM using wearable sensors and internet-of-things-based monitoring applications. Appl Clin Inform 2021; 12: 1-9.
Zee B, Lee J, Lai M, et al. Digital solution for detection of undiagnosed diabetes using machine learning-based retinal image analysis. BMJ Open Diabetes Res Care 2022; 10: e002914.
Li J, Yuan P, Hu X, et al. A tongue features fusion approach to predicting prediabetes and diabetes with machine learning. J Biomed Inform 2021; 115: 03693.
Zhou W, Sailani MR, Contrepois K, et al. Longitudinal multi-omics of host-microbe dynamics in prediabetes. Nature 2019; 569: 663-71.
Samadi S, Rashid M, Turksoy K, et al. Automatic detection and estimation of unannounced meals for multivariable artificial pancreas system. Diabetes Technol Ther 2018; 20: 235-46.
Nimri R, Oron T, Muller I, et al. Adjustment of insulin pump settings in type 1 diabetes management: advisor pro device compared to physicians’ recommendations. J Diabetes Sci Technol 2022; 16: 364-72.
Abraham SB, Arunachalam S, Zhong A, Agrawal P, Cohen O, McMahon CM. Improved real-world glycemic control with continuous glucose monitoring system predictive alerts. J Diabetes Sci Technol 2021; 15: 91-7.
Rollo ME, Aguiar EJ, Williams RL, et al. eHealth technologies to support nutrition and physical activity behaviors in diabetes self-management. Metab Syndr Obes 2016; 9: 381-90.
Shah VN, Garg SK. Managing diabetes in the digital age. Clin Diabetes Endocrinol 2015; 1: 16.
Patel MS, Asch DA, Volpp KG. Wearable devices as facilitators, not drivers, of health behavior change. JAMA 2015; 313: 459-60.
Frøisland DH, Årsand E. Integrating visual dietary documentation in mobilephone-based self-management application for adolescents with type 1 diabetes. J Diabetes Sci Technol 2015; 9: 541-8.
Berman MA, Guthrie NL, Edwards KL, et al. Change in glycemic control with use of a digital therapeutic in adults with type 2 diabetes: cohort study. JMIR Diabetes 2018; 3: e4.
Osborn CY, van Ginkel JR, Rodbard D, et al. One drop | mobile: an evaluation of hemoglobin A1c improvement linked to app engagement. JMIR Diabetes 2017; 2: e21.
Abràmoff MD, Lou Y, Erginay A, et al. Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Invest Ophthalmol Vis Sci 2016; 57: 5200-6.
Abràmoff MD, Lavin PT, Birch M, Shah N, Folk JC. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. NPJ Digit Med 2018; 1: 39.
Abràmoff MD, Folk JC, Han DP, et al. Automated analysis of retinal images for detection of referable diabetic retinopathy. JAMA Ophthalmol 2013; 131: 351-7.
Heydon P, Egan C, Bolter L, et al. Prospective evaluation of an artificial intelligence-enabled algorithm for automated diabetic retinopathy screening of 30 000 patients. Br J Ophthalmol 2021; 105: 723-8.
Ipp E, Liljenquist D, Bode B, et al.; EyeArt Study Group. Pivotal evaluation of an artificial intelligence system for autonomous detection of referrable and vision-threatening diabetic retinopathy. JAMA Netw Open 2021; 4: e2134254.
Ting DSW, Cheung CYL, Lim G, et al. Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. JAMA 2017; 318: 2211-23.
Bellemo V, Lim ZW, Lim G, et al. Artificial intelligence using deep learning to screen for referable and vision-threatening diabetic retinopathy in Africa: a clinical validation study. Lancet Digit Health 2019; 1: e35-44.
Gulshan V, Peng L, Coram M, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 2016; 316: 2402-10.
Raumviboonsuk P, Krause J, Chotcomwongse P, et al. Deep learning versus human graders for classifying diabetic retinopathy severity in a nationwide screening program. NPJ Digit Med 2019; 2: 25.
Ruamviboonsuk P, Tiwari R, Sayres R, et al. Real-time diabetic retinopathy screening by deep learning in a multisite national screening programme: a prospective interventional cohort study. Lancet Digit Health 2022; 4: e235-44.
Li Z, Keel S, Liu C, et al. An automated grading system for detection of vision-threatening referable diabetic retinopathy on the basis of color fundus photographs. Diabetes Care 2018; 41: 2509-16.
Public Health England. NHS Diabetic Eye Screening Programme: grading definitions for referable disease. Accessed 14 April 2023. Available from
Zhang Y, Shi J, Peng Y, et al. Artificial intelligence-enabled screening for diabetic retinopathy: a real-world, multicenter and prospective study. BMJ Open Diabetes Res Care 2020; 8: e001596.
Yang Y, Pan J, Yuan M, et al. Performance of the AIDRScreening system in detecting diabetic retinopathy in the fundus photographs of Chinese patients: a prospective, multicenter, clinical study. Ann Transl Med 2022; 10: 1088.
Singh K, Singh VK, Agrawal NK, Gupta SK, Singh K. Association of Toll-like receptor 4 polymorphisms with diabetic foot ulcers and application of artificial neural network in DFU risk assessment in type 2 diabetes patients. Biomed Res Int 2013; 2013: 318686.
Khandakar A, Chowdhury MEH, Reaz MBI, et al. A novel machine learning approach for severity classification of diabetic foot complications using thermogram images. Sensors (Basel) 2022; 22: 4249.
Cruz-Vega I, Hernandez-Contreras D, Peregrina-Barreto H, Rangel-Magdaleno JJ, Ramirez-Cortes JM. Deep learning classification for diabetic foot thermograms. Sensors (Basel) 2020; 20: 1762.
Arteaga-Marrero N, Hernández A, Villa E, González-Pérez S, Luque C, Ruiz-Alzola J. Segmentation approaches for diabetic foot disorders. Sensors (Basel) 2021; 21: 934.
Davia M, Germani M, Mandolini M, Mengoni M, Montiel E, Raffaeli R. Shoes customization design tools for the “diabetic foot”. CADA 2011; 8: 693-711.
Zequera M, Stephan S, Paul J. Effectiveness of moulded insoles in reducing plantar pressure in diabetic patients. Annu Int Conf IEEE Eng Med Biol Soc 2007; 2007: 4671-4.
Journals System - logo
Scroll to top