REVIEW
The promise of artificial intelligence in diabetes management – a narrative review
 
 
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Department of Medicine, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
 
 
Submission date: 2026-02-05
 
 
Acceptance date: 2026-05-15
 
 
Online publication date: 2026-07-24
 
 
Corresponding author
Khalid Alfares   

Internal Medicine, King Abdulaziz University, Saudi Arabia
 
 
 
KEYWORDS
TOPICS
ABSTRACT
The incorporation of artificial intelligence (AI) into diabetes management marks a paradigm shift in healthcare, addressing significant limitations of traditional approaches such as financial limits, clinical stagnation, and insufficient access to healthcare services. Healthcare providers can improve long-term patient outcomes by leveraging AI-driven innovations like predictive analytics, continu ous glucose monitoring, automated insulin delivery systems, and clinical decision support systems. Furthermore, AI applications in diabetic complications screening, medical nutrition therapy, and per sonalized exercise regimens help to provide more comprehensive and individualized diabetes care. By addressing important shortcomings of conventional methods, this review examined the revolu tionary potential of AI in the management of diabetes.
REFERENCES (92)
1.
GBD 2021 Diabetes Collaborators. Global, regional, and national burden of diabetes from 1990 to 2021, with pro¬jections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021. Lancet 2023; 402: 203-234.
 
2.
Cho NH, Shaw JE, Karuranga S, Huang Y, da Rocha Fer¬nandes JD, Ohlrogge AW, et al. IDF Diabetes atlas: global estimates of diabetes prevalence for 2017 and projec¬tions for 2045. Diabetes Res Clin Pract 2018; 138: 271-281.
 
3.
Papatheodorou K, Banach M, Bekiari E, Rizzo M, Ed¬monds M. Complications of diabetes 2017. J Diabetes Res 2018; 2018: 3086167-3086171.
 
4.
Karachaliou F, Simatos G, Simatou A. The challenges in the development of diabetes prevention and care mod¬els in low-income settings. Front Endocrinol (Lausanne) 2020; 11: 518-527.
 
5.
Herges JR, Neumiller JJ, McCoy RG. Easing the financial burden of diabetes management: a guide for patients and primary care clinicians. 2021. Clin Diabetes 2021; 39: 427-436.
 
6.
Siegel KR, Ali MK, Zhou X, Ng BP, Jawanda S, Proia K, et al. Cost-effectiveness of interventions to manage diabetes: has the evidence changed since 2008?. Diabetes Care 2020; 43: 1557-1592.
 
7.
Eseadi C, Amedu AN, Ilechukwu LC, Ngwu MO, Ossai OV. Accessibility and utilization of healthcare services among diabetic patients: is diabetes a poor man’s ailment? World J Diabetes 2023; 14: 1493-1501.
 
8.
Eliot KA, L’Horset AM, Gibson K, Petrosky S. Interprofes¬sional education and collaborative practice in nutrition and dietetics 2020: an update. J Acad Nutr Diet 2021; 121: 637-646.
 
9.
Lingow SE, Guyton JE. Clinical inertia among health care providers in a public health safety-net clinic in the man¬agement of type 2 diabetes. J Am Pharm Assoc 2020; 60: 734-739.
 
10.
Khalifa M, Albadawy M. Artificial intelligence for diabetes: enhancing prevention, diagnosis, and effective manage¬ment. Comput Method Program Biomed Update 2024; 5: 100141.
 
11.
Ellahham S. Artificial intelligence: the future for diabetes care. Am J Med 2020; 133: 895-900.
 
12.
Musacchio N, Giancaterini A, Guaita G, Ozzello A, Pel¬legrini MA, Ponzani P, et al. Artificial intelligence and big data in diabetes care: a position statement of the Italian Association of Medical Diabetologists. J Med Internet Res 2020; 22: e16922.
 
13.
Sheng B, Pushpanathan K, Guan Z, Lim QH, Lim ZW, Yew SME, et al. Artificial intelligence for diabetes care: current and future prospects. Lancet Diabetes Endo¬crinol 2024; 12: 569-595.
 
14.
Interaction design foundation. Artificial intelligence (AI). 2025. Availabl from: https://www.interaction-design... literature/topics/AI.
 
15.
Maleki Varnosfaderani S, Forouzanfar M. The Role of AI in hospitals and clinics: transforming healthcare in the 21st century. Bioengineering (Basel) 2024; 11: 337-375.
 
16.
IBM. Understanding the different types of artificial intelli¬gence. Available from: 2025. https://www.ibm.com/think/ topics/artificial-intelligence-types.
 
17.
Kosilek RP, Schopohl J, Grunke M, Reincke M, Dimopou¬lou C, Stalla GK, et al. Automatic face classification of Cushing’s syndrome in women – a novel screening ap¬proach. Exp Clin Endocrinol Diabetes 2013; 121: 561-564.
 
18.
Kong X, Gong S, Su L, Howard N, Kong Y. Automatic detection of acromegaly from facial photographs us¬ing machine learning methods. EBioMedicine 2018; 27: 94-102.
 
19.
Ludwig M, Ludwig B, Mikuła A, Biernat S, Rudnicki J, Kaliszewski K. The Use of artificial intelligence in the di¬agnosis and classification of thyroid nodules: an update. Cancers (Basel) 2023; 15: 708-732.
 
20.
Guan Z, Li H, Liu R, Cai C, Liu Y, Li Jet al. Artificial intelli¬gence in diabetes management: advancements, opportu¬nities, and challenges. Cell Rep Med 2023; 4: 101213.
 
21.
Rajalakshmi R, Subashini R, Anjana RM, Mohan V. Au¬tomated diabetic retinopathy detection in smart¬phone-based fundus photography using artificial intel¬ligence. Eye (Lond) 2018; 32: 1138-1144.
 
22.
Al-Absi HRH, Pai A, Naeem U, Mohamed FK, Arya S, Sbe¬it RA, et al. DiaNet v2 deep learning based method for diabetes diagnosis using retinal images. Sci Rep 2024; 14: 1595.
 
23.
Wang J, Wang YX, Zeng D, Zhu Z, Li D, Liu Y, et al. Ar¬tificial intelligence-enhanced retinal imaging as a bi¬omarker for systemic diseases. Theranostics 2025; 15: 3223-3233.
 
24.
Channa R, Wolf RM, Abràmoff MD, Lehmann HP. Effec¬tiveness of artificial intelligence screening in preventing vision loss from diabetes: a policy model. NPJ Digit Med 2023; 6: 53. .
 
25.
Rajesh AE, Davidson OQ, Lee CS, Lee AY. Artificial intelli¬gence and diabetic retinopathy: AI framework, prospec¬tive studies, head-to-head validation, and cost-effective¬ness. Diabetes Care 2023; 46: 1728-1739.
 
26.
Zhang W, Yu Q, Siddiquie B, Divakaran A, Sawhney H. “Snap-n-Eat”: food recognition and nutrition estimation on a smartphone. J Diabetes Sci Technol 2015; 9: 525-533.
 
27.
Vasiloglou MF, Mougiakakou S, Aubry E, Bokelmann A, Fricker R, Gomes F, et al. A comparative study on car¬bohydrate estimation: GoCARB vs. dietitians. Nutrients 2018; 10: 741-752.
 
28.
Alowais SA, Alghamdi SS, Alsuhebany N, Alqahtani T, Al¬shaya AI, Almohareb SN, et al. Revolutionizing health¬care: the role of artificial intelligence in clinical practice. BMC Med Educ 2023; 23: 689-694.
 
29.
Sasaki Y, Sato K, Kobayashi S, Asakura K. Nutrient and food group prediction as orchestrated by an automated image recognition system in a smartphone App (CALO mama): validation study. JMIR Form Res 2022; 6: e31875.
 
30.
Dang J, Liu L. Dehumanization risks associated with arti¬ficial intelligence use. Am Psychol 2025.
 
31.
Detopoulou P, Voulgaridou G, Moschos P, Levidi D, Ana¬stasiou T, Dedes V, et al. Artificial intelligence, nutrition, and ethical issues: a mini-review. Clin Nutr Open Sci 2023; 50: 46-56.
 
32.
Sak J, Suchodolska M. Artificial intelligence in nutrients science research: a review. Nutrients 2021; 13: 322-338.
 
33.
Lu Y, Stathopoulou T, Vasiloglou MF, Pinault LF, Kiley C, Spanakis EK, et al. goFOODTM: an artificial intelligence system for dietary assessment. Sensors (Basel) 2020; 20: 4283-4301.
 
34.
Calvaresi D, Carli R, Piguet JG, Contreras VH, Luzzani G, Najjar A, et al. Ethical and legal considerations for nutrition virtual coaches. AI Ethics 2022. Available from: file:///C:/Users/wan/Downloads/Calvaresi_2022_ethi¬cal.pdf.
 
35.
Detopoulou P, Aggeli M, Andrioti E, Detopoulou M. Ma¬cronutrient content and food exchanges for 48 Greek Mediterranean dishes. Nutr Diet 2017; 74: 200-209.
 
36.
Klaic M, Kapp S, Hudson P, Chapman W, Denehy L, Story D, et al. Implementability of healthcare interventions: an overview of reviews and development of a conceptual framework. Implement Sci 2022; 17: 10-30.
 
37.
Binagwaho A, Frisch MF, Udoh K, Drown L, Ntawukuriry¬ayo JT, Nkurunziza D, et al.. Implementation research: an efficient and effective tool to accelerate universal health coverage. Int J Health Policy Manag 2020; 9: 182-184.
 
38.
Vettoretti M, Cappon G, Facchinetti A, Sparacino G. Ad¬vanced diabetes management using artificial intelligence and continuous glucose monitoring sensors. Sensors (Ba¬sel) 2020; 20: 3870-3888.
 
39.
Everett E, Kane B, Yoo A, Dobs A, Mathioudakis N. A nov¬el approach for fully automated, personalized health coaching for adults with prediabetes: pilot clinical trial. J Med Internet Res 2018; 20: e72.
 
40.
Sun T, Xu Y, Xie H, Ma Z, Wang Y. Intelligent personalized exercise prescription based on an ehealth promotion system to improve health outcomes of middle-aged and older adult community dwellers: pretest-posttest study. J Med Internet Res 2021; 23: e28221.
 
41.
Rasa AR. Artificial intelligence and its revolutionary role in physical and mental rehabilitation: a review of recent advancements. Biomed Res Int 2024; 2024: 9554590.
 
42.
Gowda RV. AI integration in personalized physical thera¬py programs. Int J Sci Res Eng Trends 2025; 11: 1-7.
 
43.
Ahmed A, Aziz S, Abd-Alrazaq A, Farooq F, Sheikh J. Overview of artificial intelligence-driven wearable devic¬es for diabetes: scoping review. J Med Internet Res 2022; 24: e36010.
 
44.
Facchinetti A, Sparacino G, Guerra S, Luijf YM, DeVries JH, Mader Jk, et al. Real-time improvement of continuous glucose monitoring accuracy: the smart sensor concept. Diabetes Care 2013; 36: 793-800.
 
45.
Martens T, Beck RW, Bailey R, Ruedy KJ, Calhoun P, Pe¬ters 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: 2262-2272.
 
46.
Van Doorn WPTM, Foreman YD, Schaper NC, Savelberg HHCM, Koster A , van der Kallen CJH, et al. Machine learning-based glucose prediction with use of continu¬ous glucose and physical activity monitoring data: the Maastricht study. PLoS One 2021; 16: e0253125.
 
47.
Sherr JL, Heinemann L, Fleming GA, Bergenstal RM, Brut¬tomesso D, Hanaire H, et al. Automated insulin delivery: benefits, challenges, and recommendations. A consensus report of the joint diabetes technology working group of the European Association for the study of Diabetes and the American Diabetes Association. Diabetologia 2023; 66: 3-22.
 
48.
Terblanche NHD. Artificial intelligence (AI) coaching: re¬defining people development and organizational perfor-mance. J Appl Behavior Sci 2024; 60: 631-638.
 
49.
American Diabetes Association Professional Practice Committee. Diabetes technology: standards of care in diabetes-2025. Diabetes Care 2025; 48: S146-S166.
 
50.
American Diabetes Association Professional Practice Committee. Pharmacologic approaches to glycemic treatment: standards of care in diabetes-2025. Diabetes Care 2025; 48: S181-S206.
 
51.
Seget S, Tekielak A, Rusak E, Jarosz-Chobot P. Commer¬cial hybrid closed-loop systems available for a patient with type 1 diabetes in 2022. Pediatr Endocrinol Diabetes Metab 2023; 29: 30-36.
 
52.
Carlson AL, Sherr JL, Shulman DI, Garg SK, Pop-Busui R, Bode BW, et al. Safety and glycemic outcomes during the MiniMed™ advanced hybrid closed-loop system pivotal trial in adolescents and adults with type 1 diabetes. Dia¬betes Technol Ther 2022; 24: 178-189.
 
53.
Beck RW, Kanapka LG, Breton MD, Brown SA, Wadwa RP, Buckingham BA, et al. A meta-analysis of randomized trial outcomes for the t: slim X2 insulin pump with Con¬trol-IQ technology in youth and adults from age 2 to 72. Diabetes Technol Ther 2023; 25: 329-342.
 
54.
Forlenza GP, DeSalvo DJ, Aleppo G, Wilmot EG, Berget C, Huyett LM, et al. Real-world evidence of Omnipod® 5 au¬tomated insulin delivery system use in 69,902 people with type 1 diabetes. Diabetes Technol Ther 2024; 26: 514-525.
 
55.
Lum JW, Bailey RJ, Barnes-Lomen V, Naranjo D, Hood KK, Lal RA, Arbiter B, et al. A real-world prospective study of the safety and effectiveness of the loop open source au-tomated insulin delivery system. Diabetes Technol Ther 2021; 23: 367-375.
 
56.
Braune K, Hussain S, Lal R. The first regulatory clearance of an open-source automated insulin delivery algorithm. J Diabetes Sci Technol 2023; 17: 1139-1141.
 
57.
Daniels J, Herrero P, Georgiou P. A Deep learning frame¬work for automatic meal detection and estimation in artificial pancreas systems. Sensors (Basel) 2022; 22: 466-474.
 
58.
Samadi S, Rashid M, Turksoy K, Feng J, Hajizadeh I, Hobbs N, et al. Automatic detection and estimation of unan¬nounced meals for multivariable artificial pancreas sys¬tem. Diabetes Technol Ther 2018; 20: 235-246.
 
59.
El-Khatib FH, Balliro C , Hillard MA, Magyar KL , Ekhlaspour L, Sinha M, et al. Home use of a bihormonal bionic pancreas versus insulin pump therapy in adults with type 1 diabetes: a multicentre randomised crossover trial. Lancet 2017; 389: 369-380.
 
60.
Russell SJ, Beck RW, Damiano ER, El-Khatib FH, Ruedy KJ, Balliro CA, et al. Multicenter, randomized trial of a bi¬onic pancreas in type 1 diabetes. N Engl J Med 2022; 387: 1161-1172.
 
61.
Tapak L, Mahjub H, Hamidi O, Poorolajal J. Real-data comparison of data mining methods in prediction of di¬abetes in iran. Healthc. Inform Res 2013; 19: 177-185.
 
62.
Maniruzzaman M, Kumar N, Menhazul Abedin M, Shaykhul Islam M, Suri HS, El-Baz AS, et al. Comparative approaches for classification of diabetes mellitus data: machine learning paradigm. Comput Method Progr Bi¬omed 2017; 152: 23-34.
 
63.
Shu T, Zhang B, Yan Tang Y. An extensive analysis of vari¬ous texture feature extractors to detect diabetes melli¬tus using facial specific regions. Comput Biol Med 2017; 83: 69-83.
 
64.
Li J, Yuan P, Hu X, Huang J, Cui L, Cui J, et al. A tongue features fusion approach to predicting prediabetes and diabetes with machine learning. J Biomed Inform 2021; 115: 103693.
 
65.
Zhang K, Liu X, Xu J, Yuan J, Cai W, Chen T, et al. Deep-learning models for the detection and incidence prediction of chronic kidney disease and type 2 diabe¬tes from retinal fundus images. Nat Biomed Eng 2021; 5: 533-545.
 
66.
Dong Z, Wang Q, Ke Y, Zhang W, Hong Q, Liu C, et al. Prediction of 3-year risk of diabetic kidney disease using machine learning based on electronic medical records. J Transl Med 2022; 20: 143-153.
 
67.
Shi S, Gao L, Zhang J, Zhang B, Xiao J, Xu W, et al. The automatic detection of diabetic kidney disease from ret¬inal vascular parameters combined with clinical variables using artificial intelligence in type-2 diabetes patients. BMC Med Inform Decis Mak 2023; 23: 241-251.
 
68.
Mengarelli A, Tigrini A, Verdini F, Scattolini M, Mobar¬ak R, Burattini L , et al. A Computer-aided screening solution for the identification of diabetic neuropathy from standing balance by leveraging multi-domain features. IEEE Trans Neural Syst Rehabil Eng 2024; 32: 2388-2397.
 
69.
Meng Y, Preston FG, Ferdousi M, Azmi S, Petropoulos IN, Kaye S, et al. Artificial intelligence based analysis of cor¬neal confocal microscopy images for diagnosing periph-eral neuropathy: a binary classification model. J Clin Med 2023; 12: 1284-1291.
 
70.
Akkus G, Sert M. Diabetic foot ulcers: a devastating com¬plication of diabetes mellitus continues non-stop in spite of new medical treatment modalities. World J Dia¬betes 2022; 13: 1106-1121.
 
71.
Khandakar A, Chowdhury MEH, Reaz MBI, Ali SHM, Kiranyaz S, Rahman T, et al. A Novel machine learning approach for severity classification of diabetic foot com¬plications using thermogram images. Sensors (Basel) 2022; 22: 4249-4262.
 
72.
Elhaddad M, Hamam S. AI-driven clinical decision sup¬port systems: an ongoing pursuit of potential. Cureus 2024; 16: e57728.
 
73.
Tarumi S, Takeuchi W, Chalkidis G, Rodriguez-Loya S, Ku¬wata J, Flynn M, et al. Leveraging artificial intelligence to improve chronic disease care: methods and application to pharmacotherapy decision support for type-2 diabe¬tes mellitus. Methods Inf Med 2021; 60: e32-e43.
 
74.
Ljubic B, Hai AA, Stanojevic M, Diaz W, Polimac D, Pav¬lovski M, et al. Predicting complications of diabetes mel¬litus using advanced machine learning algorithms. J Am Med Inform Assoc 2020; 27: 1343-1351.
 
75.
Quirós C, Giménez M, Giménez M, Conget I. Real-world effectiveness of the MiniMed 780G advanced hybrid closed-loop system after 6 months of use. Endocrinol Diabetes Nutr (Engl Ed) 2023; 70: 512-519.
 
76.
Kanapka LG, Wadwa RP, Bre ton MD, Ruedy KJ , Ekhlaspour L, Forlenza GP, et al. Extended use of the Control-IQ closed-loop control system in children with type 1 diabetes. Diabetes Care 2021; 44: 473-478.
 
77.
Brown SA, Forlenza GP, Bode BW, Pinsker JE, Levy CJ, Criego AB, et al. Multicenter trial of a tubeless, on-body automated insulin delivery system with customizable glycemic targets in pediatric and adult participants with type 1 diabetes. Diabetes Care 2021; 44: 1630-1640.
 
78.
Geukes Foppen RJ, Gioia V, Gupta S, Johnson CL, Giantsi¬dis J, Papademetris M. Methodology for safe and secure AI in diabetes management. J Diabetes Sci Technol 2024; 6: 19322968241304434.
 
79.
Raman R, Dasgupta D, Ramasamy K, George R, Mohan V, Ting D. Using artificial intelligence for diabetic retinopa¬thy screening: policy implications. Indian J Ophthalmol 2021; 69: 2993-2998.
 
80.
Balasubramaniam N, Kauppinen M, Rannisto A, Hiek¬kanen K, Kujala S. Transparency and explainability of AI systems: from ethical guidelines to requirements. Inf Software Technol 2023; 159: 107197.
 
81.
Martin KD, Zimmermann J. Artificial intelligence and its implications for data privacy. Curr Opin Psychol 2024; 58: 101829.
 
82.
Harishbhai Tilala M, Kumar Chenchala P, Choppadandi A, Kaur J, Naguri S, Saoji R, et al. Ethical considerations in the use of artificial intelligence and machine learning in health care: a comprehensive review. Cureus 2024; 16: e62443.
 
83.
Hassan M, Kushniruk A, Borycki E. Barriers to and facil¬itators of artificial intelligence adoption in health care: scoping review. JMIR Hum Factors 2024; 11: e48633.
 
84.
Ciecierski-Holmes T, Singh R, Axt M, Brenner S, Barteit S. Artificial intelligence for strengthening healthcare sys¬tems in low- and middle-income countries: a systematic scoping review. NPJ Digit Med 2022; 5: 162-136.
 
85.
Wallis L , Blessing P, Dalwai M, Shin SD. Integrating mHealth at point of care in low- and middle-income set¬tings: the system perspective. Glob Health Action 2017; 10: 1327686.
 
86.
Khan MS, Umer H, Faruqe F. Artificial intelligence for low income countries. Hum Soc Sci Commun 2024: 11: 1422.
 
87.
Fagherazzi G, Goetzinger C, Rashid MA, Aguayo GA, Hu¬iart L. Digital Health Strategies to fight COVID-19 world¬wide: challenges, recommendations, and a call for pa¬pers. J Med Internet Res 2020; 22: e19284.
 
88.
Arora A, Barrett M, Lee E, Oborn E, Prince K. Risk and the future of AI: algorithmic bias, data colonialism, and marginalization. Inform Organ 2023; 33: 100478.
 
89.
Das DA, Kumar S, Hussain A, Reddy BR. Diabetes predic¬tion using ensemble learning techniques. Procedia Comp Sci 2025; 258: 3155-3164.
 
90.
Gulshan V, Peng L , Coram M, Stumpe MC, Wu D, Narayanaswamy A, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 2016; 316: 2402-2410.
 
91.
Fox I, Lee J, Pop-Busui R, Wiens J. Deep reinforcement learning for closed-loop blood glucose control. Proc Ma¬chine Learn Res 2020; 126: 1-28.
 
92.
Turchin A. Natural language processing for diabetes dig¬ital health. In: Klonoff DC, Kerr D, Espinoza J (eds.). Dia¬betes digital health, telehealth, and artificial intelligence. Academic Press, Cambridge, Massachusetts 2024, 341-351.
 
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