Explore our sister journals here.

Maintenance Notice

Due to necessary scheduled maintenance, the JMIR Publications website will be unavailable from Wednesday, July 01, 2020 at 8:00 PM to 10:00 PM EST. We apologize in advance for any inconvenience this may cause you.

Who will be affected?

Citing this Article

Right click to copy or hit: ctrl+c (cmd+c on mac)

Published on 30.05.18 in Vol 20, No 5 (2018): May

This paper is in the following e-collection/theme issue:

Works citing "Artificial Intelligence for Diabetes Management and Decision Support: Literature Review"

Ivan Contreras, Josep Vehi

J Med Internet Res 2018 (May 30); 20(5):e10775 

According to Crossref, the following articles are citing this article (DOI 10.2196/10775):

(note that this is only a small subset of citations)

  1. Lunenfeld B, Bilger W, Longobardi S, Kirsten J, D’Hooghe T, Sunkara SK. Decision points for individualized hormonal stimulation with recombinant gonadotropins for treatment of women with infertility. Gynecological Endocrinology 2019;35(12):1027
    CrossRef
  2. . Digital Health Consumers on the Road to the Future. Journal of Medical Internet Research 2019;21(11):e16359
    CrossRef
  3. Agrawal A, Gans JS, Goldfarb A. Artificial Intelligence: The Ambiguous Labor Market Impact of Automating Prediction. Journal of Economic Perspectives 2019;33(2):31
    CrossRef
  4. Shokrekhodaei M, Quinones S. Review of Non-Invasive Glucose Sensing Techniques: Optical, Electrical and Breath Acetone. Sensors 2020;20(5):1251
    CrossRef
  5. Tejedor M, Woldaregay AZ, Godtliebsen F. Reinforcement learning application in diabetes blood glucose control: A systematic review. Artificial Intelligence in Medicine 2020;104:101836
    CrossRef
  6. Preethi P, Asokan R. Modelling LSUTE: PKE Schemes for Safeguarding Electronic Healthcare Records Over Cloud Communication Environment. Wireless Personal Communications 2021;117(4):2695
    CrossRef
  7. Ambagtsheer R, Shafiabady N, Dent E, Seiboth C, Beilby J. The application of artificial intelligence (AI) techniques to identify frailty within a residential aged care administrative data set. International Journal of Medical Informatics 2020;136:104094
    CrossRef
  8. Bertachi A, Viñals C, Biagi L, Contreras I, Vehí J, Conget I, Giménez M. Prediction of Nocturnal Hypoglycemia in Adults with Type 1 Diabetes under Multiple Daily Injections Using Continuous Glucose Monitoring and Physical Activity Monitor. Sensors 2020;20(6):1705
    CrossRef
  9. Abd-Alrazaq A, Alajlani M, Alhuwail D, Schneider J, Al-Kuwari S, Shah Z, Hamdi M, Househ M. Artificial Intelligence in the Fight Against COVID-19: Scoping Review. Journal of Medical Internet Research 2020;22(12):e20756
    CrossRef
  10. Agrawal A, Gans JS, Goldfarb A. Artificial Intelligence: The Ambiguous Labor Market Impact of Automating Prediction. SSRN Electronic Journal 2019;
    CrossRef
  11. Broome DT, Hilton CB, Mehta N. Policy Implications of Artificial Intelligence and Machine Learning in Diabetes Management. Current Diabetes Reports 2020;20(2)
    CrossRef
  12. Sosunkevic S, Rapalis A, Marozas M, Ceponis J, Lukosevicius A. Diabetic Vascular Damage: Review of Pathogenesis and Possible Evaluation Technologies. IEEE Access 2019;7:148511
    CrossRef
  13. Taylor KA, Forlenza GP. Use of Machine Learning and Hybrid Closed Loop Insulin Delivery at Diabetes Camps. Diabetes Technology & Therapeutics 2020;22(7):535
    CrossRef
  14. Asgari S, Scalzo F, Kasprowicz M. Pattern Recognition in Medical Decision Support. BioMed Research International 2019;2019:1
    CrossRef
  15. Li J, Liang JY, Laken SJ, Langer R, Traverso G. Clinical Opportunities for Continuous Biosensing and Closed-Loop Therapies. Trends in Chemistry 2020;2(4):319
    CrossRef
  16. Oviedo S, Contreras I, Bertachi A, Quirós C, Giménez M, Conget I, Vehi J. Minimizing postprandial hypoglycemia in Type 1 diabetes patients using multiple insulin injections and capillary blood glucose self-monitoring with machine learning techniques. Computer Methods and Programs in Biomedicine 2019;178:175
    CrossRef
  17. Channa R, Wolf R, Abramoff MD. Autonomous Artificial Intelligence in Diabetic Retinopathy: From Algorithm to Clinical Application. Journal of Diabetes Science and Technology 2021;15(3):695
    CrossRef
  18. Massaro A, Maritati V, Giannone D, Convertini D, Galiano A. LSTM DSS Automatism and Dataset Optimization for Diabetes Prediction. Applied Sciences 2019;9(17):3532
    CrossRef
  19. Ljubic B, Hai AA, Stanojevic M, Diaz W, Polimac D, Pavlovski M, Obradovic Z. Predicting complications of diabetes mellitus using advanced machine learning algorithms. Journal of the American Medical Informatics Association 2020;27(9):1343
    CrossRef
  20. . Artificial Intelligence: The Future for Diabetes Care. The American Journal of Medicine 2020;133(8):895
    CrossRef
  21. Nagaraj SB, Sidorenkov G, van Boven JFM, Denig P. Predicting short‐ and long‐term glycated haemoglobin response after insulin initiation in patients with type 2 diabetes mellitus using machine‐learning algorithms. Diabetes, Obesity and Metabolism 2019;21(12):2704
    CrossRef
  22. Hosseini MM, Zargoush M, Alemi F, Kheirbek RE. Leveraging machine learning and big data for optimizing medication prescriptions in complex diseases: a case study in diabetes management. Journal of Big Data 2020;7(1)
    CrossRef
  23. Rajšp A, Fister I. A Systematic Literature Review of Intelligent Data Analysis Methods for Smart Sport Training. Applied Sciences 2020;10(9):3013
    CrossRef
  24. Woldaregay AZ, Årsand E, Botsis T, Albers D, Mamykina L, Hartvigsen G. Data-Driven Blood Glucose Pattern Classification and Anomalies Detection: Machine-Learning Applications in Type 1 Diabetes. Journal of Medical Internet Research 2019;21(5):e11030
    CrossRef
  25. Kerr D, Klonoff DC. Digital Diabetes Data and Artificial Intelligence: A Time for Humility Not Hubris. Journal of Diabetes Science and Technology 2019;13(1):123
    CrossRef
  26. Triantafyllidis AK, Tsanas A. Applications of Machine Learning in Real-Life Digital Health Interventions: Review of the Literature. Journal of Medical Internet Research 2019;21(4):e12286
    CrossRef
  27. Kim YJ, Kelley BP, Nasser JS, Chung KC. Implementing Precision Medicine and Artificial Intelligence in Plastic Surgery: Concepts and Future Prospects. Plastic and Reconstructive Surgery - Global Open 2019;7(3):e2113
    CrossRef
  28. Silva KD, Lee WK, Forbes A, Demmer RT, Barton C, Enticott J. Use and performance of machine learning models for type 2 diabetes prediction in community settings: A systematic review and meta-analysis. International Journal of Medical Informatics 2020;143:104268
    CrossRef
  29. Reddy S, Fox J, Purohit MP. Artificial intelligence-enabled healthcare delivery. Journal of the Royal Society of Medicine 2019;112(1):22
    CrossRef
  30. Vehí J, Contreras I, Oviedo S, Biagi L, Bertachi A. Prediction and prevention of hypoglycaemic events in type-1 diabetic patients using machine learning. Health Informatics Journal 2020;26(1):703
    CrossRef
  31. Wang J, Warnecke J, Haghi M, Deserno T. Unobtrusive Health Monitoring in Private Spaces: The Smart Vehicle. Sensors 2020;20(9):2442
    CrossRef
  32. . Increasing the Impact of JMIR Journals in the Attention Economy. Journal of Medical Internet Research 2019;21(10):e16172
    CrossRef
  33. Vettoretti M, Cappon G, Facchinetti A, Sparacino G. Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors. Sensors 2020;20(14):3870
    CrossRef
  34. Ibrahim M, Baker J, Cahn A, Eckel RH, El Sayed NA, Fischl AH, Gaede P, Leslie RD, Pieralice S, Tuccinardi D, Pozzilli P, Richelsen B, Roitman E, Standl E, Toledano Y, Tuomilehto J, Weber SL, Umpierrez GE. Hypoglycaemia and its management in primary care setting. Diabetes/Metabolism Research and Reviews 2020;36(8)
    CrossRef
  35. Khodaei MJ, Candelino N, Mehrvarz A, Jalili N. Physiological Closed-Loop Control (PCLC) Systems: Review of a Modern Frontier in Automation. IEEE Access 2020;8:23965
    CrossRef
  36. Bruno A, Johnston KC, Durkalski-Mauldin VL. Treatment of Hyperglycemia in Patients With Acute Stroke—Reply. JAMA 2019;322(22):2248
    CrossRef
  37. Li J, Huang J, Zheng L, Li X. Application of Artificial Intelligence in Diabetes Education and Management: Present Status and Promising Prospect. Frontiers in Public Health 2020;8
    CrossRef
  38. Musacchio N, Giancaterini A, Guaita G, Ozzello A, Pellegrini MA, Ponzani P, Russo GT, Zilich R, de Micheli A. Artificial Intelligence and Big Data in Diabetes Care: A Position Statement of the Italian Association of Medical Diabetologists. Journal of Medical Internet Research 2020;22(6):e16922
    CrossRef
  39. Elhadd T, Mall R, Bashir M, Palotti J, Fernandez-Luque L, Farooq F, Mohanadi DA, Dabbous Z, Malik RA, Abou-Samra AB. Artificial Intelligence (AI) based machine learning models predict glucose variability and hypoglycaemia risk in patients with type 2 diabetes on a multiple drug regimen who fast during ramadan (The PROFAST – IT Ramadan study). Diabetes Research and Clinical Practice 2020;169:108388
    CrossRef
  40. Martinez-Millana A, Jarones E, Fernandez-Llatas C, Hartvigsen G, Traver V. App Features for Type 1 Diabetes Support and Patient Empowerment: Systematic Literature Review and Benchmark Comparison. JMIR mHealth and uHealth 2018;6(11):e12237
    CrossRef
  41. Javaid M, Haleem A, Khan I, Vaishya R, Vaish A. Extending capabilities of artificial intelligence for decision-making and healthcare education. Apollo Medicine 2020;17(1):53
    CrossRef
  42. Mishra DK, Shukla S. ROLE OF ARTIFICIAL INTELLIGENCE IN DIABETES MANAGEMENT. International Journal of Engineering Technologies and Management Research 2020;7(7):80
    CrossRef
  43. Zhu T, Li K, Kuang L, Herrero P, Georgiou P. An Insulin Bolus Advisor for Type 1 Diabetes Using Deep Reinforcement Learning. Sensors 2020;20(18):5058
    CrossRef
  44. . Use of Artificial Intelligence to Improve Diabetes Outcomes in Patients Using Multiple Daily Injections Therapy. Diabetes Technology & Therapeutics 2019;21(S2):S2-4
    CrossRef
  45. Abhari S, Niakan Kalhori SR, Ebrahimi M, Hasannejadasl H, Garavand A. Artificial Intelligence Applications in Type 2 Diabetes Mellitus Care: Focus on Machine Learning Methods. Healthcare Informatics Research 2019;25(4):248
    CrossRef
  46. Shen J, Chen J, Zheng Z, Zheng J, Liu Z, Song J, Wong SY, Wang X, Huang M, Fang P, Jiang B, Tsang W, He Z, Liu T, Akinwunmi B, Wang CC, Zhang CJP, Huang J, Ming W. An Innovative Artificial Intelligence–Based App for the Diagnosis of Gestational Diabetes Mellitus (GDM-AI): Development Study. Journal of Medical Internet Research 2020;22(9):e21573
    CrossRef
  47. Stechova K, Hlubik J, Pithova P, Cikl P, Lhotska L. Comprehensive Analysis of the Real Lifestyles of T1D Patients for the Purpose of Designing a Personalized Counselor for Prandial Insulin Dosing. Nutrients 2019;11(5):1148
    CrossRef
  48. Guemes A, Cappon G, Hernandez B, Reddy M, Oliver N, Georgiou P, Herrero P. Predicting Quality of Overnight Glycaemic Control in Type 1 Diabetes Using Binary Classifiers. IEEE Journal of Biomedical and Health Informatics 2020;24(5):1439
    CrossRef
  49. Carson JM, Chakshu NK, Sazonov I, Nithiarasu P. Artificial intelligence approaches to predict coronary stenosis severity using non-invasive fractional flow reserve. Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine 2020;234(11):1337
    CrossRef
  50. . Better Patient Outcomes Through Mining of Biomedical Big Data. Frontiers in ICT 2018;5
    CrossRef
  51. Mujahid O, Contreras I, Vehi J. Machine Learning Techniques for Hypoglycemia Prediction: Trends and Challenges. Sensors 2021;21(2):546
    CrossRef
  52. Fujihara K, Matsubayashi Y, Harada Yamada M, Yamamoto M, Iizuka T, Miyamura K, Hasegawa Y, Maegawa H, Kodama S, Yamazaki T, Sone H. Machine Learning Approach to Decision Making for Insulin Initiation in Japanese Patients With Type 2 Diabetes (JDDM 58): Model Development and Validation Study. JMIR Medical Informatics 2021;9(1):e22148
    CrossRef
  53. Li Y, Cao H, Allen CM, Wang X, Erdelez S, Shyu C. Computational modeling of human reasoning processes for interpretable visual knowledge: a case study with radiographers. Scientific Reports 2020;10(1)
    CrossRef
  54. Roos T, Hochstadt S, Keuthage W, Kröger J, Lueg A, Mühlen H, Schütte L, Scheper N, Ehrmann D, Hermanns N, Heinemann L, Kulzer B. Level of Digitalization in Germany: Results of the Diabetes Digitalization and Technology (D.U.T) Report 2020. Journal of Diabetes Science and Technology 2022;16(1):144
    CrossRef
  55. Wang W, Pei X, Zhang L, Chen Z, Lin D, Duan X, Fan J, Pan Q, Guo L. Application of new international classification of adult‐onset diabetes in Chinese inpatients with diabetes mellitus. Diabetes/Metabolism Research and Reviews 2021;37(7)
    CrossRef
  56. Wang Z, Wang J, Kahkoska AR, Buse JB, Gu Z. Developing Insulin Delivery Devices with Glucose Responsiveness. Trends in Pharmacological Sciences 2021;42(1):31
    CrossRef
  57. van der Waa J, Nieuwburg E, Cremers A, Neerincx M. Evaluating XAI: A comparison of rule-based and example-based explanations. Artificial Intelligence 2021;291:103404
    CrossRef
  58. Borle NC, Ryan EA, Greiner R. The challenge of predicting blood glucose concentration changes in patients with type I diabetes. Health Informatics Journal 2021;27(1):146045822097758
    CrossRef
  59. Aggarwal N, Ahmed M, Basu S, Curtin JJ, Evans BJ, Matheny ME, Nundy S, Sendak MP, Shachar C, Shah RU, Thadaney-Israni S. Advancing Artificial Intelligence in Health Settings Outside the Hospital and Clinic. NAM Perspectives 2020;
    CrossRef
  60. Şahin A, Aydın A. Personalized Advanced Time Blood Glucose Level Prediction. Arabian Journal for Science and Engineering 2021;46(10):9333
    CrossRef
  61. Kalaiselvan V, Sharma A, Gupta SK. “Feasibility test and application of AI in healthcare”—with special emphasis in clinical, pharmacovigilance, and regulatory practices. Health and Technology 2021;11(1):1
    CrossRef
  62. Zhang Y, Yu H, Dong R, Ji X, Li F, Jiang L. Application Prospect of Artificial Intelligence in Rehabilitation and Management of Myasthenia Gravis. BioMed Research International 2021;2021:1
    CrossRef
  63. Khanam JJ, Foo SY. A comparison of machine learning algorithms for diabetes prediction. ICT Express 2021;7(4):432
    CrossRef
  64. Pham Q, Gamble A, Hearn J, Cafazzo JA. The Need for Ethnoracial Equity in Artificial Intelligence for Diabetes Management: Review and Recommendations. Journal of Medical Internet Research 2021;23(2):e22320
    CrossRef
  65. Abokhzam AA, Gupta NK, Bose DK. Efficient diabetes mellitus prediction with grid based random forest classifier in association with natural language processing. International Journal of Speech Technology 2021;24(3):601
    CrossRef
  66. Contreras I, Calm R, Sainz MA, Herrero P, Vehi J. Combining Grammatical Evolution with Modal Interval Analysis: An Application to Solve Problems with Uncertainty. Mathematics 2021;9(6):631
    CrossRef
  67. Mouchabac S, Leray P, Adrien V, Gollier-Briant F, Bonnot O. Prevention of Suicidal Relapses in Adolescents With a Smartphone Application: Bayesian Network Analysis of a Preclinical Trial Using In Silico Patient Simulations. Journal of Medical Internet Research 2021;23(9):e24560
    CrossRef
  68. Rodriguez-León C, Villalonga C, Munoz-Torres M, Ruiz JR, Banos O. Mobile and Wearable Technology for the Monitoring of Diabetes-Related Parameters: Systematic Review. JMIR mHealth and uHealth 2021;9(6):e25138
    CrossRef
  69. Dave D, Erraguntla M, Lawley M, DeSalvo D, Haridas B, McKay S, Koh C. Improved Low-Glucose Predictive Alerts Based on Sustained Hypoglycemia: Model Development and Validation Study. JMIR Diabetes 2021;6(2):e26909
    CrossRef
  70. Kim K, Yang H, Yi J, Son H, Ryu J, Kim YC, Jeong JC, Chin HJ, Na KY, Chae D, Han SS, Kim S. Real-Time Clinical Decision Support Based on Recurrent Neural Networks for In-Hospital Acute Kidney Injury: External Validation and Model Interpretation. Journal of Medical Internet Research 2021;23(4):e24120
    CrossRef
  71. Yin J, Ngiam KY, Teo HH. Role of Artificial Intelligence Applications in Real-Life Clinical Practice: Systematic Review. Journal of Medical Internet Research 2021;23(4):e25759
    CrossRef
  72. Douer N, Meyer J. Theoretical, Measured, and Subjective Responsibility in Aided Decision Making. ACM Transactions on Interactive Intelligent Systems 2021;11(1):1
    CrossRef
  73. Meneghetti L, Dassau E, Doyle FJ, Del Favero S. Machine Learning-Based Anomaly Detection Algorithms to Alert Patients Using Sensor Augmented Pump of Infusion Site Failures. Journal of Diabetes Science and Technology 2022;16(3):641
    CrossRef
  74. Roski J, Maier EJ, Vigilante K, Kane EA, Matheny ME. Enhancing trust in AI through industry self-governance. Journal of the American Medical Informatics Association 2021;28(7):1582
    CrossRef
  75. Niu X, Chi J, Guo J, Ruan H, Zhang J, Tao H, Wang Y. Effects of nurse-led web-based interventions on people with type 2 diabetes mellitus: A systematic review and meta-analysis. Journal of Telemedicine and Telecare 2021;27(5):269
    CrossRef
  76. Tarumi S, Takeuchi W, Chalkidis G, Rodriguez-Loya S, Kuwata J, Flynn M, Turner KM, Sakaguchi FH, Weir C, Kramer H, Shields DE, Warner PB, Kukhareva P, Ban H, Kawamoto K. Leveraging Artificial Intelligence to Improve Chronic Disease Care: Methods and Application to Pharmacotherapy Decision Support for Type-2 Diabetes Mellitus. Methods of Information in Medicine 2021;60(S 01):e32
    CrossRef
  77. Biagi L, Bertachi A, Giménez M, Conget I, Bondia J, Martín-Fernández JA, Vehí J. Probabilistic Model of Transition between Categories of Glucose Profiles in Patients with Type 1 Diabetes Using a Compositional Data Analysis Approach. Sensors 2021;21(11):3593
    CrossRef
  78. Fan Y, Long E, Cai L, Cao Q, Wu X, Tong R. Machine Learning Approaches to Predict Risks of Diabetic Complications and Poor Glycemic Control in Nonadherent Type 2 Diabetes. Frontiers in Pharmacology 2021;12
    CrossRef
  79. Enriquez JS, Chu Y, Pudakalakatti S, Hsieh KL, Salmon D, Dutta P, Millward NZ, Lurie E, Millward S, McAllister F, Maitra A, Sen S, Killary A, Zhang J, Jiang X, Bhattacharya PK, Shams S. Hyperpolarized Magnetic Resonance and Artificial Intelligence: Frontiers of Imaging in Pancreatic Cancer. JMIR Medical Informatics 2021;9(6):e26601
    CrossRef
  80. Liu C, Tang S, An K, Zhang S, Zhou Y, Su N, Yang R, Liao X, An Z, Li S. Knowledge, Attitude, and Practice of Metformin Extended-Release Tablets Among Clinicians in China: A Cross-Sectional Survey. Frontiers in Pharmacology 2021;12
    CrossRef
  81. Klimontov VV, Berikov VB, Saik OV. Artificial intelligence in diabetology. Diabetes mellitus 2021;24(2):156
    CrossRef
  82. Nguyen M, Jankovic I, Kalesinskas L, Baiocchi M, Chen JH. Machine learning for initial insulin estimation in hospitalized patients. Journal of the American Medical Informatics Association 2021;28(10):2212
    CrossRef
  83. Zhu T, Li K, Herrero P, Georgiou P. Deep Learning for Diabetes: A Systematic Review. IEEE Journal of Biomedical and Health Informatics 2021;25(7):2744
    CrossRef
  84. Rhee SY, Sung JM, Kim S, Cho I, Lee S, Chang H. Development and Validation of a Deep Learning Based Diabetes Prediction System Using a Nationwide Population-Based Cohort. Diabetes & Metabolism Journal 2021;45(4):515
    CrossRef
  85. Gautier T, Ziegler LB, Gerber MS, Campos-Náñez E, Patek SD. Artificial intelligence and diabetes technology: A review. Metabolism 2021;124:154872
    CrossRef
  86. Alloatti F, Bosca A, Di Caro L, Pieraccini F. Diabetes and conversational agents: the AIDA project case study. Discover Artificial Intelligence 2021;1(1)
    CrossRef
  87. Khurana A, Leffler DA, Gomez K, Thukral C. Short and long-term follow-up and clinical outcomes in patients with celiac disease in a large private practice setting. BMC Gastroenterology 2023;23(1)
    CrossRef
  88. Huang J, Yeung AM, Armstrong DG, Battarbee AN, Cuadros J, Espinoza JC, Kleinberg S, Mathioudakis N, Swerdlow MA, Klonoff DC. Artificial Intelligence for Predicting and Diagnosing Complications of Diabetes. Journal of Diabetes Science and Technology 2023;17(1):224
    CrossRef
  89. Yu Z, Luo W, Tse R, Pau G. DMNet: A Personalized Risk Assessment Framework for Elderly People With Type 2 Diabetes. IEEE Journal of Biomedical and Health Informatics 2023;27(3):1558
    CrossRef
  90. Nemat H, Khadem H, Eissa MR, Elliott J, Benaissa M. Blood Glucose Level Prediction: Advanced Deep-Ensemble Learning Approach. IEEE Journal of Biomedical and Health Informatics 2022;26(6):2758
    CrossRef
  91. Khor SM, Choi J, Won P, Ko SH. Challenges and Strategies in Developing an Enzymatic Wearable Sweat Glucose Biosensor as a Practical Point-Of-Care Monitoring Tool for Type II Diabetes. Nanomaterials 2022;12(2):221
    CrossRef
  92. Park G, Lee H, Lee M. Artificial Intelligence-based Healthcare Interventions: A Systematic Review. Korean Journal of Adult Nursing 2021;33(5):427
    CrossRef
  93. Jia W, Fisher EB. Application and prospect of artificial intellingence in diabetes care. Medical Review 2023;3(1):102
    CrossRef
  94. Torrent-Sellens J, Jiménez-Zarco AI, Saigí-Rubió F. Do People Trust in Robot-Assisted Surgery? Evidence from Europe. International Journal of Environmental Research and Public Health 2021;18(23):12519
    CrossRef
  95. Ossai CI, Wickramasinghe N. Sentiments prediction and thematic analysis for diabetes mobile apps using Embedded Deep Neural Networks and Latent Dirichlet Allocation. Artificial Intelligence in Medicine 2023;138:102509
    CrossRef
  96. Fiedorova K, Augustynek M, Kubicek J, Kudrna P, Bibbo D. Review of present method of glucose from human blood and body fluids assessment. Biosensors and Bioelectronics 2022;211:114348
    CrossRef
  97. Novitski P, Cohen CM, Karasik A, Shalev V, Hodik G, Moskovitch R. All-cause mortality prediction in T2D patients with iTirps. Artificial Intelligence in Medicine 2022;130:102325
    CrossRef
  98. Almásy MG, Hörömpő A, Kiss D, Kertész G, Batyrshin I, Gomide F, Kreinovich V, Shahbazova S. A review on modeling tumor dynamics and agent reward functions in reinforcement learning based therapy optimization. Journal of Intelligent & Fuzzy Systems 2022;43(6):6939
    CrossRef
  99. Novitski P, Cohen CM, Karasik A, Hodik G, Moskovitch R. Temporal patterns selection for All-Cause Mortality prediction in T2D with ANNs. Journal of Biomedical Informatics 2022;134:104198
    CrossRef
  100. Sikalidis AK, Kristo AS, Reaves SK, Kurfess FJ, DeLay AM, Vasilaky K, Donegan L. Capacity Strengthening Undertaking—Farm Organized Response of Workers against Risk for Diabetes: (C.S.U.—F.O.R.W.A.R.D. with Cal Poly)—A Concept Approach to Tackling Diabetes in Vulnerable and Underserved Farmworkers in California. Sensors 2022;22(21):8299
    CrossRef
  101. Gudlavalleti RH, Xi X, Legassey A, Chan P, Li J, Burgess D, Giardina C, Papadimitrakopoulos F, Jain F. Highly Miniaturized, Low-Power CMOS ASIC Chip for Long-Term Continuous Glucose Monitoring. Journal of Diabetes Science and Technology 2023;:193229682311534
    CrossRef
  102. . Wie profitieren Menschen mit Diabetes von Big Data und künstlicher Intelligenz?. Der Diabetologe 2021;17(8):799
    CrossRef
  103. . WITHDRAWN: Real-Time Monitoring Of Big Data Sports Teaching Data Based On Complex Embedded System. Microprocessors and Microsystems 2022;:104181
    CrossRef
  104. Dénes-Fazakas L, Siket M, Szilágyi L, Kovács L, Eigner G. Detection of Physical Activity Using Machine Learning Methods Based on Continuous Blood Glucose Monitoring and Heart Rate Signals. Sensors 2022;22(21):8568
    CrossRef
  105. Qiu F, Li J, Zhang R, Legerlotz K. Use of artificial neural networks in the prognosis of musculoskeletal diseases—a scoping review. BMC Musculoskeletal Disorders 2023;24(1)
    CrossRef
  106. Allam A, Feuerriegel S, Rebhan M, Krauthammer M. Analyzing Patient Trajectories With Artificial Intelligence. Journal of Medical Internet Research 2021;23(12):e29812
    CrossRef
  107. Annuzzi G, Apicella A, Arpaia P, Bozzetto L, Criscuolo S, Benedetto ED, Pesola M, Prevete R, Vallefuoco E. Impact of Nutritional Factors in Blood Glucose Prediction in Type 1 Diabetes Through Machine Learning. IEEE Access 2023;11:17104
    CrossRef
  108. Makroum MA, Adda M, Bouzouane A, Ibrahim H. Machine Learning and Smart Devices for Diabetes Management: Systematic Review. Sensors 2022;22(5):1843
    CrossRef
  109. Zhang J, Zheng C, Yang J, Usama M. Research on an Interactive Question Answering System of Artificial Intelligence Customer Service Based on Word2Vec. International Journal of e-Collaboration 2022;18(2):1
    CrossRef
  110. Zhu T, Li K, Herrero P, Georgiou P. Basal Glucose Control in Type 1 Diabetes Using Deep Reinforcement Learning: An In Silico Validation. IEEE Journal of Biomedical and Health Informatics 2021;25(4):1223
    CrossRef
  111. Bisio A, Anderson S, Norlander L, O’Malley G, Robic J, Ogyaadu S, Hsu L, Levister C, Ekhlaspour L, Lam DW, Levy C, Buckingham B, Breton MD. Impact of a Novel Diabetes Support System on a Cohort of Individuals With Type 1 Diabetes Treated With Multiple Daily Injections: A Multicenter Randomized Study. Diabetes Care 2022;45(1):186
    CrossRef
  112. Alqahtani A, Roy A. Application of Artificial Intelligence in Discovery and Development of Anticancer and Antidiabetic Therapeutic Agents. Evidence-Based Complementary and Alternative Medicine 2022;2022:1
    CrossRef
  113. Thomsen CHN, Hangaard S, Kronborg T, Vestergaard P, Hejlesen O, Jensen MH. Time for Using Machine Learning for Dose Guidance in Titration of People With Type 2 Diabetes? A Systematic Review of Basal Insulin Dose Guidance. Journal of Diabetes Science and Technology 2022;:193229682211459
    CrossRef
  114. Della Cioppa A, De Falco I, Koutny T, Scafuri U, Ubl M, Tarantino E. Reducing high-risk glucose forecasting errors by evolving interpretable models for Type 1 diabetes. Applied Soft Computing 2023;134:110012
    CrossRef
  115. Zaidi SMA, Chandola V, Ibrahim M, Romanski B, Mastrandrea LD, Singh T. Multi-step ahead predictive model for blood glucose concentrations of type-1 diabetic patients. Scientific Reports 2021;11(1)
    CrossRef
  116. Linan-Reyes M, Garrido-Zafra J, Gil-de-Castro A, Moreno-Munoz A. Energy Management Expert Assistant, a New Concept. Sensors 2021;21(17):5915
    CrossRef
  117. Abiyev RH, Altiparmak H, Abdullah L. Type-2 Fuzzy Neural System for Diagnosis of Diabetes. Mathematical Problems in Engineering 2021;2021:1
    CrossRef
  118. Holzer R, Bloch W, Brinkmann C. Continuous Glucose Monitoring in Healthy Adults—Possible Applications in Health Care, Wellness, and Sports. Sensors 2022;22(5):2030
    CrossRef
  119. Singla R, Aggarwal S, Bindra J, Garg A, Singla A. Developing clinical decision support system using machine learning methods for type 2 diabetes drug management. Indian Journal of Endocrinology and Metabolism 2022;26(1):44
    CrossRef
  120. Balakrishnan P, Jacyshyn-Owen E, Eberl M, Friedrich B, Etter T. Real-world demographic patterns of users of a digital primary prevention service for diabetes. Cardiovascular Endocrinology & Metabolism 2022;12(1)
    CrossRef
  121. Parcerisas A, Contreras I, Delecourt A, Bertachi A, Beneyto A, Conget I, Viñals C, Giménez M, Vehi J. A Machine Learning Approach to Minimize Nocturnal Hypoglycemic Events in Type 1 Diabetic Patients under Multiple Doses of Insulin. Sensors 2022;22(4):1665
    CrossRef
  122. Noguer J, Contreras I, Mujahid O, Beneyto A, Vehi J. Generation of Individualized Synthetic Data for Augmentation of the Type 1 Diabetes Data Sets Using Deep Learning Models. Sensors 2022;22(13):4944
    CrossRef
  123. Mosa ASM, Thongmotai C, Islam H, Paul T, Hossain KSMT, Mandhadi V. Evaluation of machine learning applications using real-world EHR data for predicting diabetes-related long-term complications. Journal of Business Analytics 2022;5(2):141
    CrossRef
  124. Sepúlveda M, Arauna D, García F, Albala C, Palomo I, Fuentes E. Frailty in Aging and the Search for the Optimal Biomarker: A Review. Biomedicines 2022;10(6):1426
    CrossRef
  125. Vargas E, Aiello EM, Pinsker JE, Teymourian H, Tehrani F, Church MM, Laffel LM, Doyle FJ, Patti M, Dassau E, Wang J. Development of a Novel Insulin Sensor for Clinical Decision-Making. Journal of Diabetes Science and Technology 2023;17(4):1029
    CrossRef
  126. Vaz MJ, Lopes J, Peixoto H, Santos MF. Predictive Analytics to support diabetic patient detection. Procedia Computer Science 2022;201:690
    CrossRef
  127. César de Lima Araújo H, Silva Martins F, Tucunduva Philippi Cortese T, Locosselli GM. Artificial intelligence in urban forestry—A systematic review. Urban Forestry & Urban Greening 2021;66:127410
    CrossRef
  128. Lee AYL, Wong AKC, Hung TTM, Yan J, Yang S. Nurse-led Telehealth Intervention for Rehabilitation (Telerehabilitation) Among Community-Dwelling Patients With Chronic Diseases: Systematic Review and Meta-analysis. Journal of Medical Internet Research 2022;24(11):e40364
    CrossRef
  129. Aktaş Yılmaz B, Yılmaz AE. Artificial Intelligence Applications in Endocrinology. Journal of Ankara University Faculty of Medicine 2022;75(1):35
    CrossRef
  130. Russo S, Bonassi S. Prospects and Pitfalls of Machine Learning in Nutritional Epidemiology. Nutrients 2022;14(9):1705
    CrossRef
  131. Kushwaha S, Srivastava R, Jain R, Sagar V, Aggarwal AK, Bhadada SK, Khanna P. Harnessing machine learning models for non-invasive pre-diabetes screening in children and adolescents. Computer Methods and Programs in Biomedicine 2022;226:107180
    CrossRef
  132. Deniz-Garcia A, Fabelo H, Rodriguez-Almeida AJ, Zamora-Zamorano G, Castro-Fernandez M, Alberiche Ruano MDP, Solvoll T, Granja C, Schopf TR, Callico GM, Soguero-Ruiz C, Wägner AM. Quality, Usability, and Effectiveness of mHealth Apps and the Role of Artificial Intelligence: Current Scenario and Challenges. Journal of Medical Internet Research 2023;25:e44030
    CrossRef
  133. . Efficacy of Wearable Devices to Measure and Promote Physical Activity in the Management of Diabetes. EMJ Diabetes 2018;:62
    CrossRef
  134. Gosak L, Martinović K, Lorber M, Stiglic G. Artificial intelligence based prediction models for individuals at risk of multiple diabetic complications: A systematic review of the literature. Journal of Nursing Management 2022;30(8):3765
    CrossRef
  135. Faccioli S, Prendin F, Facchinetti A, Sparacino G, Del Favero S. Combined Use of Glucose-Specific Model Identification and Alarm Strategy Based on Prediction-Funnel to Improve Online Forecasting of Hypoglycemic Events. Journal of Diabetes Science and Technology 2023;17(5):1295
    CrossRef
  136. Cooke EA, Smith NA, Thomas SA, Ruston C, Hothi S, Hughes D. An integrated discrete event simulation and particle swarm optimisation model for optimising efficiency of cancer diagnosis pathways. Healthcare Analytics 2022;2:100082
    CrossRef
  137. Celik I, Dindar M, Muukkonen H, Järvelä S. The Promises and Challenges of Artificial Intelligence for Teachers: a Systematic Review of Research. TechTrends 2022;66(4):616
    CrossRef
  138. Yilmaz EC, Belue MJ, Turkbey B, Reinhold C, Choyke PL. A Brief Review of Artificial Intelligence in Genitourinary Oncological Imaging. Canadian Association of Radiologists Journal 2023;74(3):534
    CrossRef
  139. Cabrera A, Biagi L, Beneyto A, Estremera E, Contreras I, Giménez M, Conget I, Bondia J, Martín-Fernández JA, Vehí J. Validation of a Probabilistic Prediction Model for Patients with Type 1 Diabetes Using Compositional Data Analysis. Mathematics 2023;11(5):1241
    CrossRef
  140. Shuvo MMH, Islam SK. Deep Multitask Learning by Stacked Long Short-Term Memory for Predicting Personalized Blood Glucose Concentration. IEEE Journal of Biomedical and Health Informatics 2023;27(3):1612
    CrossRef
  141. Chen L, Jiang M, Jia F, Liu G. Artificial intelligence adoption in business-to-business marketing: toward a conceptual framework. Journal of Business & Industrial Marketing 2022;37(5):1025
    CrossRef
  142. Tuppad A, Patil SD. Machine learning for diabetes clinical decision support: a review. Advances in Computational Intelligence 2022;2(2)
    CrossRef
  143. Vervoort D, Tam DY, Wijeysundera HC. Health Technology Assessment for Cardiovascular Digital Health Technologies and Artificial Intelligence: Why Is It Different?. Canadian Journal of Cardiology 2022;38(2):259
    CrossRef
  144. Pleus S, Freckmann G, Schauer S, Heinemann L, Ziegler R, Ji L, Mohan V, Calliari LE, Hinzmann R. Self-Monitoring of Blood Glucose as an Integral Part in the Management of People with Type 2 Diabetes Mellitus. Diabetes Therapy 2022;13(5):829
    CrossRef
  145. Camp EJ, Quon RJ, Sajatovic M, Briggs F, Brownrigg B, Janevic MR, Meisenhelter S, Steimel SA, Testorf ME, Kiriakopoulos E, Mazanec MT, Fraser RT, Johnson EK, Jobst BC. Supervised machine learning to predict reduced depression severity in people with epilepsy through epilepsy self-management intervention. Epilepsy & Behavior 2022;127:108548
    CrossRef
  146. Imrisek SD, Lee M, Goldner D, Nagra H, Lavaysse LM, Hoy-Rosas J, Dachis J, Sears LE. Effects of a Novel Blood Glucose Forecasting Feature on Glycemic Management and Logging in Adults With Type 2 Diabetes Using One Drop: Retrospective Cohort Study. JMIR Diabetes 2022;7(2):e34624
    CrossRef
  147. Oprescu A, Miró-Amarante G, García-Díaz L, Rey V, Chimenea-Toscano A, Martínez-Martínez R, Romero-Ternero M. Towards a data collection methodology for Responsible Artificial Intelligence in health: A prospective and qualitative study in pregnancy. Information Fusion 2022;83-84:53
    CrossRef
  148. Mujahid O, Contreras I, Beneyto A, Conget I, Giménez M, Vehi J. Conditional Synthesis of Blood Glucose Profiles for T1D Patients Using Deep Generative Models. Mathematics 2022;10(20):3741
    CrossRef
  149. Xie Y, Lu L, Gao F, He S, Zhao H, Fang Y, Yang J, An Y, Ye Z, Dong Z. Integration of Artificial Intelligence, Blockchain, and Wearable Technology for Chronic Disease Management: A New Paradigm in Smart Healthcare. Current Medical Science 2021;41(6):1123
    CrossRef
  150. Joshua SR, Abbas W, Lee J. M-Healthcare Model: An Architecture for a Type 2 Diabetes Mellitus Mobile Application. Applied Sciences 2022;13(1):8
    CrossRef
  151. Kalra S, Unnikrishnan AG, Prasanna Kumar KM, Sahay R, Chandalia HB, Saboo B, Annamalai S, Kesavadev J, Shukla R, Wangnoo SK, Baruah MP, Jacob J, Arora S, Singla R, Sharma SK, Damodaran S, Bantwal G. Addendum 1: Forum for Injection Technique and Therapy Expert Recommendations, India. Diabetes Therapy 2023;14(1):29
    CrossRef
  152. Ahmed A, Aziz S, Abd-alrazaq A, Farooq F, Sheikh J. Overview of Artificial Intelligence–Driven Wearable Devices for Diabetes: Scoping Review. Journal of Medical Internet Research 2022;24(8):e36010
    CrossRef
  153. Kumar Das S, Nayak KK, Krishnaswamy PR, Kumar V, Bhat N. Review—Electrochemistry and Other Emerging Technologies for Continuous Glucose Monitoring Devices. ECS Sensors Plus 2022;1(3):031601
    CrossRef
  154. Sharma V, Feldman M, Sharma R. Telehealth Technologies in Diabetes Self-management and Education. Journal of Diabetes Science and Technology 2024;18(1):148
    CrossRef
  155. Juneja D, Gupta A, Singh O. Artificial intelligence in critically ill diabetic patients: current status and future prospects. Artificial Intelligence in Gastroenterology 2022;3(2):66
    CrossRef
  156. Nabukenya J, Egwar AA, Drumright L, Semwanga AR, Kasasa S. Feasibility and utility of Point-of-Care electronic clinical data capture in Uganda’s healthcare system: a qualitative study. Journal of the American Medical Informatics Association 2023;30(5):932
    CrossRef
  157. Yang Y, Xu F, Chen J, Tao C, Li Y, Chen Q, Tang S, Lee HK, Shen W. Artificial intelligence-assisted smartphone-based sensing for bioanalytical applications: A review. Biosensors and Bioelectronics 2023;229:115233
    CrossRef
  158. Ansari RM, Harris MF, Hosseinzadeh H, Zwar N. Application of Artificial Intelligence in Assessing the Self-Management Practices of Patients with Type 2 Diabetes. Healthcare 2023;11(6):903
    CrossRef
  159. Hu X, Li X, Wen S, Chen L. Predictive Modeling the Probability of Suffering from Metabolic Syndrome Using Machine Learning: A Population-Based Study. SSRN Electronic Journal 2022;
    CrossRef
  160. Hu X, Li X, Wen S, Chen L. Predictive Modeling the Probability of Suffering from Metabolic Syndrome Using Machine Learning: A Population-Based Study. SSRN Electronic Journal 2022;
    CrossRef
  161. Kushwaha S, Srivastava R, Jain R, Sagar V, Aggarwal AK, Bhadada SK, Khanna P. Harnessing Machine Learning Models for Non-Invasive Pre-Diabetes Screening in Children and Adolescents. SSRN Electronic Journal 2022;
    CrossRef
  162. Carpinteiro C, Lopes J, Abelha A, Santos MF. A Comparative Study of Classification Algorithms for Early Detection of Diabetes. Procedia Computer Science 2023;220:868
    CrossRef
  163. Della Cioppa A, De Falco I, Koutny T, Scafuri U, Ubl M, Tarantino E. Reducing High-Risk Glucose Forecasting Errors by Evolving Interpretable Models for Type 1 Diabetes. SSRN Electronic Journal 2022;
    CrossRef
  164. Mounadel A, Ech-Cheikh H, Lissane Elhaq S, Rachid A, Sadik M, Abdellaoui B. Application of artificial intelligence techniques in municipal solid waste management: a systematic literature review. Environmental Technology Reviews 2023;12(1):316
    CrossRef
  165. Robinson R, Liday C, Lee S, Williams IC, Wright M, An S, Nguyen E. Artificial Intelligence in Health Care—Understanding Patient Information Needs and Designing Comprehensible Transparency: Qualitative Study. JMIR AI 2023;2:e46487
    CrossRef
  166. Juneja D, Deepak D, Nasa P. What, why and how to monitor blood glucose in critically ill patients. World Journal of Diabetes 2023;14(5):528
    CrossRef
  167. Rangel-Peña UJ, Zárate-Hernández LA, Camacho-Mendoza RL, Gómez-Castro CZ, González-Montiel S, Pescador-Rojas M, Meneses-Viveros A, Cruz-Borbolla J. Conceptual DFT, machine learning and molecular docking as tools for predicting LD50 toxicity of organothiophosphates. Journal of Molecular Modeling 2023;29(7)
    CrossRef
  168. Cabrera A, Estremera E, Beneyto A, Biagi L, Contreras I, Martín-Fernández JA, Vehí J. Individualized Prediction of Blood Glucose Outcomes Using Compositional Data Analysis. Mathematics 2023;11(21):4517
    CrossRef
  169. Fujihara K, Sone H. Machine Learning Approach to Drug Treatment Strategy for Diabetes Care. Diabetes & Metabolism Journal 2023;47(3):325
    CrossRef
  170. . Can artificial intelligence assist physicians in selecting the right medications for patients with diabetes mellitus, improve outcomes, and reduce financial burdens on health-care systems?. Advances in Biomedical and Health Sciences 2023;2(3):144
    CrossRef
  171. Liu M, Liu C, Lin T, Ma Y. Implementing a Novel Machine Learning System for Nutrition Education in Diabetes Mellitus Nutritional Clinic: Predicting 1-Year Blood Glucose Control. Bioengineering 2023;10(10):1139
    CrossRef
  172. Vargas E, Nandhakumar P, Ding S, Saha T, Wang J. Insulin detection in diabetes mellitus: challenges and new prospects. Nature Reviews Endocrinology 2023;19(8):487
    CrossRef
  173. Nabukenya J, Drumright L, Alunyu AE, Semwanga AR. Critical risk and success factors for sustainability of an electronic health data capture, processing and dissemination platform for Uganda. Health Informatics Journal 2023;29(2):146045822311805
    CrossRef
  174. Alanis AY, Sanchez OD, Vaca-González A, Rangel-Heras E. Intelligent Classification and Diagnosis of Diabetes and Impaired Glucose Tolerance Using Deep Neural Networks. Mathematics 2023;11(19):4065
    CrossRef
  175. Khodve GB, Banerjee S. Artificial Intelligence in Efficient Diabetes Care. Current Diabetes Reviews 2023;19(9)
    CrossRef
  176. Contreras I, Muñoz-Organero M, Beneyto A, Vehi J. Active Labeling Correction of Mealtimes and the Appearance of Types of Carbohydrates in Type 1 Diabetes Information Records. Mathematics 2023;11(19):4050
    CrossRef
  177. Tanhapour M, Peimani M, Rostam Niakan Kalhori S, Nasli Esfahani E, Shakibian H, Mohammadzadeh N, Qorbani M. The effect of personalized intelligent digital systems for self-care training on type II diabetes: a systematic review and meta-analysis of clinical trials. Acta Diabetologica 2023;60(12):1599
    CrossRef
  178. Zhu T, Li K, Georgiou P. Offline Deep Reinforcement Learning and Off-Policy Evaluation for Personalized Basal Insulin Control in Type 1 Diabetes. IEEE Journal of Biomedical and Health Informatics 2023;27(10):5087
    CrossRef
  179. Cai D, Wu W, Cescon M, Liu W, Ji L, Shi D. Data-enabled learning and control algorithms for intelligent glucose management: The state of the art. Annual Reviews in Control 2023;56:100897
    CrossRef
  180. Huang S, Ke X, Huang Y, Wu Y, Yu X, Liu H, Liu D. A prediction model for moderate to severe cancer-related fatigue in colorectal cancer after chemotherapy: a prospective case‒control study. Supportive Care in Cancer 2023;31(7)
    CrossRef
  181. Salvioli S, Basile MS, Bencivenga L, Carrino S, Conte M, Damanti S, De Lorenzo R, Fiorenzato E, Gialluisi A, Ingannato A, Antonini A, Baldini N, Capri M, Cenci S, Iacoviello L, Nacmias B, Olivieri F, Rengo G, Querini PR, Lattanzio F. Biomarkers of aging in frailty and age-associated disorders: State of the art and future perspective. Ageing Research Reviews 2023;91:102044
    CrossRef
  182. Ansari MA, Chauhan W, Shoaib S, Alyahya SA, Ali M, Ashraf H, Alomary MN, Al-Suhaimi EA. Emerging therapeutic options in the management of diabetes: recent trends, challenges and future directions. International Journal of Obesity 2023;47(12):1179
    CrossRef
  183. Wang R, Xiong K, Wang Z, Wu D, Hu B, Ruan J, Sun C, Ma D, Li L, Liao S. Immunodiagnosis — the promise of personalized immunotherapy. Frontiers in Immunology 2023;14
    CrossRef
  184. Li L, Cheng Y, Ji W, Liu M, Hu Z, Yang Y, Wang Y, Zhou Y. Machine learning for predicting diabetes risk in western China adults. Diabetology & Metabolic Syndrome 2023;15(1)
    CrossRef
  185. Prioleau T, Bartolome A, Comi R, Stanger C. DiaTrend: A dataset from advanced diabetes technology to enable development of novel analytic solutions. Scientific Data 2023;10(1)
    CrossRef
  186. Zou X, Liu Y, Ji L. Review: Machine learning in precision pharmacotherapy of type 2 diabetes—A promising future or a glimpse of hope?. DIGITAL HEALTH 2023;9
    CrossRef
  187. . USE OF ARTIFICIAL INTELLIGENCE IN HEALTH SERVICES MANAGEMENT IN TÜRKİYE. International Journal of Health Services Research and Policy 2023;8(2):139
    CrossRef
  188. Abdulazeem H, Whitelaw S, Schauberger G, Klug SJ, Vathy-Fogarassy . A systematic review of clinical health conditions predicted by machine learning diagnostic and prognostic models trained or validated using real-world primary health care data. PLOS ONE 2023;18(9):e0274276
    CrossRef
  189. . The 4IR-Health Service Delivery Nexus. International Journal of Public Administration in the Digital Age 2023;10(1):1
    CrossRef
  190. Mora T, Roche D, Rodríguez-Sánchez B. Predicting the onset of diabetes-related complications after a diabetes diagnosis with machine learning algorithms. Diabetes Research and Clinical Practice 2023;204:110910
    CrossRef
  191. Fujihara K, Yamada Harada M, Horikawa C, Iwanaga M, Tanaka H, Nomura H, Sui Y, Tanabe K, Yamada T, Kodama S, Kato K, Sone H. Machine learning approach to predict body weight in adults. Frontiers in Public Health 2023;11
    CrossRef
  192. Rodríguez-Rodríguez I, Campo-Valera M, Rodríguez J, Lok Woo W. IoMT innovations in diabetes management: Predictive models using wearable data. Expert Systems with Applications 2024;238:121994
    CrossRef
  193. Jaloli M, Cescon M. Reinforcement Learning for Multiple Daily Injection (MDI) Therapy in Type 1 Diabetes (T1D). BioMedInformatics 2023;3(2):422
    CrossRef
  194. Liao X, Yao C, Zhang J, Liu LZ. Recent advancement in integrating artificial intelligence and information technology with real‐world data for clinical decision‐making in China: A scoping review. Journal of Evidence-Based Medicine 2023;16(4):534
    CrossRef
  195. Zrubka Z, Kertész G, Gulácsi L, Czere J, Hölgyesi , Nezhad HM, Mosavi A, Kovács L, Butte AJ, Péntek M. The Reporting Quality of Machine Learning Studies on Pediatric Diabetes Mellitus: Systematic Review. Journal of Medical Internet Research 2024;26:e47430
    CrossRef
  196. Mackenzie SC, Sainsbury CAR, Wake DJ. Diabetes and artificial intelligence beyond the closed loop: a review of the landscape, promise and challenges. Diabetologia 2024;67(2):223
    CrossRef
  197. Wang B, Asan O, Zhang Y. Shaping the future of chronic disease management: Insights into patient needs for AI-based homecare systems. International Journal of Medical Informatics 2024;181:105301
    CrossRef
  198. García-Jaramillo M, Luque C, León-Vargas F. Machine Learning and Deep Learning Techniques Applied to Diabetes Research: A Bibliometric Analysis. Journal of Diabetes Science and Technology 2024;18(2):287
    CrossRef
  199. . ChatGPT in Diabetes Care: An Overview of the Evolution and Potential of Generative Artificial Intelligence Model Like ChatGPT in Augmenting Clinical and Patient Outcomes in the Management of Diabetes. International Journal of Diabetes and Technology 2023;2(2):66
    CrossRef
  200. Murala DK, Panda SK, Dash SP. MedMetaverse: Medical Care of Chronic Disease Patients and Managing Data Using Artificial Intelligence, Blockchain, and Wearable Devices State-of-the-Art Methodology. IEEE Access 2023;11:138954
    CrossRef
  201. Dai D, Bo M, Ren X, Dai K. Application and exploration of artificial intelligence technology in urban ecosystem-based disaster risk reduction: A scoping review. Ecological Indicators 2024;158:111565
    CrossRef
  202. Jacobs PG, Herrero P, Facchinetti A, Vehi J, Kovatchev B, Breton MD, Cinar A, Nikita KS, Doyle FJ, Bondia J, Battelino T, Castle JR, Zarkogianni K, Narayan R, Mosquera-Lopez C. Artificial Intelligence and Machine Learning for Improving Glycemic Control in Diabetes: Best Practices, Pitfalls, and Opportunities. IEEE Reviews in Biomedical Engineering 2024;17:19
    CrossRef
  203. Ahmad M, Tan M, Bergman H, Shalhoub J, Davies A. The use of artificial intelligence in three-dimensional imaging modalities and diabetic foot disease: A systematic review. JVS-Vascular Insights 2024;2:100057
    CrossRef
  204. Aldaghi T, Muzik J. Multicriteria Decision-Making in Diabetes Management and Decision Support: Systematic Review. JMIR Medical Informatics 2024;12:e47701
    CrossRef
  205. Visan AI, Negut I. Integrating Artificial Intelligence for Drug Discovery in the Context of Revolutionizing Drug Delivery. Life 2024;14(2):233
    CrossRef
  206. Khalifa M, Albadawy M. Artificial intelligence for diabetes: Enhancing prevention, diagnosis, and effective management. Computer Methods and Programs in Biomedicine Update 2024;5:100141
    CrossRef
  207. Ahmed BM, Ali ME, Masud MM, Naznin M. Recent trends and techniques of blood glucose level prediction for diabetes control. Smart Health 2024;32:100457
    CrossRef
  208. Han T, Wei W, Jiang W, Geng Y, Liu Z, Yang R, Jin C, Lei Y, Sun X, Xu J, Juan C, Sun C. The Future Landscape and Framework of Precision Nutrition. Engineering 2024;
    CrossRef
  209. Wu D, Mei Y, Sun Z, Duan H, Deng N. Multi-Feature Map Integrated Attention Model for Early Prediction of Type 2 Diabetes Using Irregular Health Examination Records. IEEE Journal of Biomedical and Health Informatics 2024;28(3):1656
    CrossRef
  210. Khalifa M, Albadawy M. Artificial Intelligence for Clinical Prediction: Exploring Key Domains and Essential Functions. Computer Methods and Programs in Biomedicine Update 2024;5:100148
    CrossRef
  211. Leal Filho W, Ribeiro PCC, Mazutti J, Lange Salvia A, Bonato Marcolin C, Lima Silva Borsatto JM, Sharifi A, Sierra J, Luetz J, Pretorius R, Viera Trevisan L. Using artificial intelligence to implement the UN sustainable development goals at higher education institutions. International Journal of Sustainable Development & World Ecology 2024;:1
    CrossRef
  212. Spoladore D, Tosi M, Lorenzini EC. Ontology-based decision support systems for diabetes nutrition therapy: A systematic literature review. Artificial Intelligence in Medicine 2024;151:102859
    CrossRef
  213. Zhang D, Wu C, Yang Z, Yin H, Liu Y, Li W, Huang H, Jin Z. The application of artificial intelligence in EUS. Endoscopic Ultrasound 2024;
    CrossRef
  214. Dénes-Fazakas L, Simon B, Hartvég , Kovács L, Dulf , Szilágyi L, Eigner G. Physical Activity Detection for Diabetes Mellitus Patients Using Recurrent Neural Networks. Sensors 2024;24(8):2412
    CrossRef

According to Crossref, the following books are citing this article (DOI 10.2196/10775):

  1. Shaban-Nejad A, Kamaleswaran R, Shin EK, Akbilgic O. Biomedical Information Technology. 2020. :197
    CrossRef
  2. Ramyashree , Venugopala PS, Barh D, Ashwini B. Advances in Artificial Intelligence and Data Engineering. 2021. Chapter 32:411
    CrossRef
  3. Kriještorac M, Halilović A, Kevric J. Advanced Technologies, Systems, and Applications IV -Proceedings of the International Symposium on Innovative and Interdisciplinary Applications of Advanced Technologies (IAT 2019). 2020. Chapter 31:388
    CrossRef
  4. . Advances in Artificial Intelligence, Software and Systems Engineering. 2021. Chapter 42:298
    CrossRef
  5. . Internet of Things Use Cases for the Healthcare Industry. 2020. Chapter 1:1
    CrossRef
  6. Contreras I, Bertachi A, Biagi L, Oviedo S, Ramkissoon C, Vehi J. Artificial Intelligence in Precision Health. 2020. :329
    CrossRef
  7. Wolkowicz KL, Doyle III FJ, Dassau E. Encyclopedia of Systems and Control. 2021. Chapter 100058-1:1
    CrossRef
  8. Agushaka JO, Ezugwu AE. Applied Informatics. 2020. Chapter 22:313
    CrossRef
  9. Jemima Jebaseeli T, Jasmine David D, Jegathesan V. Internet of Medical Things. 2021. Chapter 9:159
    CrossRef
  10. Vehi J, Mujahid O, Contreras I. Artificial Intelligence in Medicine. 2021. Chapter 158-1:1
    CrossRef
  11. Wolkowicz KL, Doyle III FJ, Dassau E. Encyclopedia of Systems and Control. 2021. Chapter 100058:312
    CrossRef
  12. Abd-Alrazaq A, Schneider J, Alhuwail D, Hamdi M, Al-Kuwari S, Al-Thani D, Househ M. Multiple Perspectives on Artificial Intelligence in Healthcare. 2021. Chapter 15:185
    CrossRef
  13. Kia N, Cavanagh J, Meacham H, Halvorsen B, Cabrera PP, Bartram T. The Fourth Industrial Revolution. 2021. Chapter 11:183
    CrossRef
  14. Segato THF, Serafim RMDS, Fernandes SES, Ralha CG. Intelligent Systems. 2021. Chapter 5:64
    CrossRef
  15. Altıparmak H, Abiyev R, Tüzünkan M. Intelligent and Fuzzy Systems. 2022. Chapter 82:739
    CrossRef
  16. Geetanjali , Malviya R, Awasthi R, Sharma PK, Kala N, Kumar V, Yadav SK. Cognitive Intelligence and Big Data in Healthcare. 2022. :349
    CrossRef
  17. Yip M, Wang Z, Gutierrez L, Foo V, Lim J, Lim G, Gunasekaran D, Wong TY, Ting DSW. Nanotechnology for Diabetes Management. 2022. :305
    CrossRef
  18. Kelly CJ, Brown APY, Taylor JA. Artificial Intelligence in Medicine. 2022. Chapter 316:1029
    CrossRef
  19. Li S, Wang J. Diabetes Digital Health and Telehealth. 2022. :201
    CrossRef
  20. Yadav S, Kaushik A, Sharma S. IoT and Cloud Computing for Societal Good. 2022. Chapter 7:101
    CrossRef
  21. Vehi J, Mujahid O, Contreras I. Advanced Bioscience and Biosystems for Detection and Management of Diabetes. 2022. Chapter 13:259
    CrossRef
  22. Kinzel C, Pfannstiel MA. Künstliche Intelligenz im Gesundheitswesen. 2022. Chapter 20:451
    CrossRef
  23. Reddy SS, Sethi N, Rajender R, Vetukuri VSR. Third International Conference on Image Processing and Capsule Networks. 2022. Chapter 13:161
    CrossRef
  24. Kelly CJ, Brown APY, Taylor JA. Artificial Intelligence in Medicine. 2021. Chapter 316-1:1
    CrossRef
  25. Ming W, He Z. Advanced Bioscience and Biosystems for Detection and Management of Diabetes. 2022. Chapter 11:227
    CrossRef
  26. Vehi J, Mujahid O, Contreras I. Artificial Intelligence in Medicine. 2022. Chapter 158:701
    CrossRef
  27. Ghosh S, Dasgupta R. Machine Learning in Biological Sciences. 2022. Chapter 14:119
    CrossRef
  28. Xanthis C, Filos D, Chouvarda I. Comprehensive Clinical Approach to Diabetes During Pregnancy. 2022. Chapter 23:431
    CrossRef
  29. Belazoui A, Telli A, Arar C. International Conference on Managing Business Through Web Analytics. 2022. Chapter 19:259
    CrossRef
  30. Simon T, Zhang J, Wang S. Advanced Information Networking and Applications. 2023. Chapter 10:113
    CrossRef
  31. Muthusamy PD, Boopathi Raja G, Sathya T, Nandhini P. Predicting Pregnancy Complications Through Artificial Intelligence and Machine Learning. 2023. chapter 18:276
    CrossRef
  32. Singh C, Thamizhamuthu R, Manjula S, Nidhya MS. AI and IoT-Based Technologies for Precision Medicine. 2023. chapter 22:380
    CrossRef
  33. El Sherbini A, Glicksberg BS, Krittanawong C. Artificial Intelligence in Clinical Practice. 2024. :15
    CrossRef
  34. . Revolutionizing Healthcare Through Artificial Intelligence and Internet of Things Applications. 2023. chapter 11:157
    CrossRef
  35. Xin Yi W, May Chong M, A/L Subarmaniyan S. Emerging Technologies for Digital Infrastructure Development. 2023. :36
    CrossRef
  36. . From Current to Future Trends in Pharmaceutical Technology. 2024. :215
    CrossRef
  37. Singh K, Barak D. Driving Smart Medical Diagnosis Through AI-Powered Technologies and Applications. 2024. chapter 8:130
    CrossRef
  38. Tornero-Costa R, Martinez-Millana A, Merino-Torres J. Explainable Artificial Intelligence and Process Mining Applications for Healthcare. 2024. Chapter 10:95
    CrossRef
  39. Sousa MJ, Sousa M, Secinaro S, Oppioli M. Proceedings of International Conference on Information Technology and Applications. 2024. Chapter 18:195
    CrossRef