Transforming Diabetes Care Through Artificial Intelligence: The Future Is Here

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Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs

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Feasibility and patient acceptability of a novel artificial intelligence-based screening model for diabetic retinopathy at endocrinology outpatient services: a pilot study

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Retinal Lesion Detection With Deep Learning Using Image Patches

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Artificial Intelligence: The Future for Diabetes Care

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Deep Learning for Diabetes: A Systematic Review

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Prediction of Type 2 Diabetes Based on Machine Learning Algorithm

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Artificial intelligence predicts the progression of diabetic kidney disease using big data machine learning

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A Brief Survey on Breast Cancer Diagnostic With Deep Learning Schemes Using Multi-Image Modalities

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Artificial Intelligence in Health Care: Current Applications and Issues

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Artificial intelligence in cardiovascular diseases: diagnostic and therapeutic perspectives

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Machine Learning and Smart Devices for Diabetes Management: Systematic Review

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Wearable devices for glucose monitoring: A review of state-of-the-art technologies and emerging trends

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Diabetes and artificial intelligence beyond the closed loop: a review of the landscape, promise and challenges

Mackenzie, S. C., Sainsbury, C. A. R., & Wake, D. J. (2023) · Diabetologia · doi:10.1007/s00125-023-06038-8 · Cited by 97

Machine Learning-Based Application for Predicting Risk of Type 2 Diabetes Mellitus (T2DM) in Saudi Arabia: A Retrospective Cross-Sectional Study

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Telehealth and Telemedicine - The Far-Reaching Medicine for Everyone and Everywhere

Wang, T.-C. (2022) · Biomedical engineering · doi:10.5772/intechopen.100663 · Cited by 84

Remote patient monitoring using artificial intelligence

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Commercial and Scientific Solutions for Blood Glucose Monitoring—A Review

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Clinical Opportunities for Continuous Biosensing and Closed-Loop Therapies

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Artificial Intelligence for Internet of Things and Enhanced Medical Systems

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Deep Physiological Model for Blood Glucose Prediction in T1DM Patients

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Coaching Through Technology: A Systematic Review into Efficacy and Effectiveness for the Ageing Population

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Meeting the Challenge of Diabetes in China

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A deep neural network with modified random forest incremental interpretation approach for diagnosing diabetes in smart healthcare

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Second-Generation Digital Health Platforms: Placing the Patient at the Center and Focusing on Clinical Outcomes

Ilan, Y. (2020) · Frontiers in Digital Health · doi:10.3389/fdgth.2020.569178 · Cited by 68

An Innovative Artificial Intelligence–Based App for the Diagnosis of Gestational Diabetes Mellitus (GDM-AI): Development Study

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Advancing Artificial Intelligence in Health Settings Outside the Hospital and Clinic

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Health Care Equity in the Use of Advanced Analytics and Artificial Intelligence Technologies in Primary Care

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Controlled Heterotypic Pseudo-Islet Assembly of Human β-Cells and Human Umbilical Vein Endothelial Cells Using Magnetic Levitation

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Role of artificial intelligence in early diagnosis and treatment of infectious diseases

Srivastava, V., Kumar, R., Wani, M. Y., Robinson, K. M., & Ahmad, A. (2024) · Scandinavian Journal of Infectious Diseases · doi:10.1080/23744235.2024.2425712 · Cited by 63

Leveraging XGBoost and explainable AI for accurate prediction of type 2 diabetes

Rafie, Z., Talab, M. S., Koor, B. E., Garavand, A., Salehnasab, C., & Ghaderzadeh, M. (2025) · BMC Public Health · doi:10.1186/s12889-025-24953-w · Cited by 57

Role of self-care in COVID-19 pandemic for people living with comorbidities of diabetes and hypertension

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Artificial Intelligence for Predicting and Diagnosing Complications of Diabetes

Huang, J. K., Yeung, A. M., Armstrong, D. G., Battarbee, A. N., Cuadros, J. A., Espinoza, J., Kleinberg, S., Mathioudakis, N. N., Swerdlow, M. A., & Klonoff, D. C. (2022) · Journal of Diabetes Science and Technology · doi:10.1177/19322968221124583 · Cited by 61

The Need for Ethnoracial Equity in Artificial Intelligence for Diabetes Management: Review and Recommendations

Phạm, Q., Gamble, A., Hearn, J., & Cafazzo, J. A. (2021) · Journal of Medical Internet Research · doi:10.2196/22320 · Cited by 53

Delivering high‐tech, AI‐based health care at Apollo Hospitals

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Exploring Nutritional Influence on Blood Glucose Forecasting for Type 1 Diabetes Using Explainable AI

Annuzzi, G., Apicella, A., Arpaïa, P., Bozzetto, L., Criscuolo, S., Benedetto, E. D., Pesola, M., & Prevete, R. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3348334 · Cited by 48

Advancing health care via artificial intelligence: From concept to clinic

Sandeep Ganesh, G., Kolusu, A. S., Prasad, K., Samudrala, P. K., & Nemmani, K. V. S. (2022) · European Journal of Pharmacology · doi:10.1016/j.ejphar.2022.175320 · Cited by 43

Artificial intelligence assisted food science and nutrition perspective for smart nutrition research and healthcare

Joshi, S., Bisht, B., Kumar, V., Singh, N., Begum, J. P. S., Singh, N., & Kumar, S. (2023) · Systems Microbiology and Biomanufacturing · doi:10.1007/s43393-023-00200-4 · Cited by 44

Diabetes: a review of its pathophysiology, and advanced methods of mitigation

Gupta, S., Sharma, N., Arora, S., & Verma, S. (2024) · Current Medical Research and Opinion · doi:10.1080/03007995.2024.2333440 · Cited by 41

A Novel Expert System for the Diagnosis and Treatment of Heart Disease

Mazhar, T., Nasir, Q., Haq, I., Kamal, M. M., Ullah, I., Kim, T., Mohamed, H. G., & Alwadai, N. M. (2022) · Electronics · doi:10.3390/electronics11233989 · Cited by 38

A systematic approach to enhance the explainability of artificial intelligence in healthcare with application to diagnosis of diabetes

Wang, Y.-C., Chen, T., & Chiu, M.-C. (2023) · Healthcare Analytics · doi:10.1016/j.health.2023.100183 · Cited by 43

Value assessment of artificial intelligence in medical imaging: a scoping review

Fasterholdt, I., Naghavi‐Behzad, M., Rasmussen, B. S., Kjølhede, T., Skjøth, M. M., Hildebrandt, M. G., & Kidholm, K. (2022) · BMC Medical Imaging · doi:10.1186/s12880-022-00918-y · Cited by 42

Artificial Intelligence for the Detection of Diabetic Retinopathy in Primary Care: Protocol for Algorithm Development

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Use of Machine Learning Approaches in Clinical Epidemiological Research of Diabetes

Basu, S., Johnson, K., & Berkowitz, S. A. (2020) · Current Diabetes Reports · doi:10.1007/s11892-020-01353-5 · Cited by 37

Health management via telemedicine: Learning from the COVID-19 experience

Sun, R., Blayney, D. W., & Hernandez‐Boussard, T. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab145 · Cited by 35

Clinician Adoption of an Artificial Intelligence Algorithm to Detect Left Ventricular Systolic Dysfunction in Primary Care.

Rushlow, D. R., Croghan, I. T., Inselman, J. W., Thacher, T. D., Friedman, P. A., Yao, X., Pellikka, P. A., Lopez-Jimenez, F., Bernard, M. E., Barry, B. A., et al. (2022) · Mayo Clinic Proceedings · doi:10.1016/j.mayocp.2022.04.008 · Cited by 33

An affective learning-based system for diagnosis and personalized management of diabetes mellitus

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Artificial intelligence perspective in the future of endocrine diseases

Hasanzad, M., Meybodi, H. R. A., Sarhangi, N., & Larijani, B. A. (2022) · Journal of Diabetes & Metabolic Disorders · doi:10.1007/s40200-021-00949-2 · Cited by 30

Diagnosis system for cancer disease using a single setting approach

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A State-of-the-Art Review of Artificial Intelligence (AI) Applications in Healthcare: Advances in Diabetes, Cancer, Epidemiology, and Mortality Prediction

Vargas-Santiago, M., León-Velasco, D. A., Maldonado-Sifuentes, C. E., & Chanona-Hernández, L. (2025) · Computers · doi:10.3390/computers14040143 · Cited by 41

Use of Artificial Intelligence to Improve Diabetes Outcomes in Patients Using Multiple Daily Injections Therapy

Forlenza, G. P. (2019) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2019.0077 · Cited by 28

Revolutionizing Healthcare: The Impact of AI‐Powered Sensors

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Capturing artificial intelligence applications’ value proposition in healthcare – a qualitative research study

Hennrich, J., Ritz, E., Hofmann, P., & Urbach, N. (2024) · BMC Health Services Research · doi:10.1186/s12913-024-10894-4 · Cited by 33

Inhibitory effects of curcumin on aldose reductase and cyclooxygenase-2 enzymes

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Recent trends and techniques of blood glucose level prediction for diabetes control

Ahmed, B. M., Ali, M. E., Masud, M., & Naznin, M. (2024) · Smart Health · doi:10.1016/j.smhl.2024.100457 · Cited by 26

Leveraging machine learning and big data for optimizing medication prescriptions in complex diseases: a case study in diabetes management

Hosseini, M. M., Zargoush, M., Alemi, F., & Kheirbek, R. (2020) · Journal Of Big Data · doi:10.1186/s40537-020-00302-z · Cited by 27

AI-Based Noninvasive Blood Glucose Monitoring: Scoping Review

Chan, P. Z., Jin, E., Jansson, M. M., & Chew, H. S. J. (2024) · Journal of Medical Internet Research · doi:10.2196/58892 · Cited by 37

Hybrid CNN-LSTM for Predicting Diabetes: A Review

Soltanizadeh, S., & Naghibi, S. S. (2023) · Current Diabetes Reviews · doi:10.2174/0115733998261151230925062430 · Cited by 25

Digital Health in Diabetes and Cardiovascular Disease

Avoke, D., Elshafeey, A., Weinstein, R. A., Kim, C. H., & Martin, S. S. (2024) · Endocrine Research · doi:10.1080/07435800.2024.2341146 · Cited by 24

POC Sensor Systems and Artificial Intelligence—Where We Are Now and Where We Are Going?

Prashanthi, K., Kovur, K. M., Rayat, D. Y., & Wishart, D. S. (2025) · Biosensors · doi:10.3390/bios15090589 · Cited by 21

Diagnostic accuracy of smartphone-based artificial intelligence systems for detecting diabetic retinopathy: A systematic review and meta-analysis

Hasan, S. U., & Siddiqui, M. A. R. (2023) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2023.110943 · Cited by 20

Artificial Intelligence in Efficient Diabetes Care

Khodve, G., & Banerjee, S. (2022) · Current Diabetes Reviews · doi:10.2174/1573399819666220905163940 · Cited by 19

Intelligent Insulin vs. Artificial Intelligence for Type 1 Diabetes: Will the Real Winner Please Stand Up?

Cambuli, V. M., & Baroni, M. G. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms241713139 · Cited by 19

Artificial Intelligence in Health Care – A Study on Perceptions of and Readiness for Artificial Intelligence in Health-care Professionals

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Machine learning in cancer prognostic and diagnostic biomarkers: A promising approach for early cancer detection

Vosoughi, P., Naghib, S. M., & Takdehghan, G. (2025) · Sensors and Actuators Reports · doi:10.1016/j.snr.2025.100385 · Cited by 19

Risk Managed Construction Strategies for Complex and Highly Regulated Healthcare Facility Developments

Ogbete, J. C., Aminu-Ibrahim, A., & Ambali, K. B. (2021) · International Journal of Scientific Research in Computer Science Engineering and Information Technology · doi:10.32628/cseit217561 · Cited by 48

Interpretable Prediction of Diabetes from Tabular Health Screening Records Using an Attentional Neural Network

Oba, Y., Tezuka, T., Sanuki, M., & Wagatsuma, Y. (2021) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa53316.2021.9564151 · Cited by 16

The Utility of Smartphone-Based Artificial Intelligence Approaches for Diabetic Retinopathy

Sheikh, A., Bhatti, A., Adeyemi, O. J., Raja, M., & Sheikh, I. (2021) · Journal of Current Ophthalmology · doi:10.4103/2452-2325.329064 · Cited by 16

Breast cancer image analysis using deep learning techniques – a survey

Koshy, S. S., Anbarasi, L. J., Jawahar, M., & Ravi, V. (2022) · Health and Technology · doi:10.1007/s12553-022-00703-5 · Cited by 19

Application of machine learning in affordable and accessible insulin management for type 1 and 2 diabetes: A comprehensive review

Eghbali‐Zarch, M., & Masoud, S. (2024) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2024.102868 · Cited by 15

Distal Symmetric Polyneuropathy Identification in Type 2 Diabetes Subjects: A Random Forest Approach

Maeda-Gutiérrez, V., Galvan-Tejada, C. E., Cruz, M., Valladares‐Salgado, A., Galván-Tejada, J. I., Gamboa-Rosales, H., García-Hernández, A., Luna-García, H., Gonzalez-Curiel, I. E., & Martinez-Acuña, M. I. (2021) · Healthcare · doi:10.3390/healthcare9020138 · Cited by 14

Benefit–Risk Assessment of ChatGPT Applications in the Field of Diabetes and Metabolic Illnesses: A Qualitative Study

Jairoun, A. A., Al‐Hemyari, S. S., Shahwan, M. J., Al‐Qirim, T. M., & Shahwan, M. (2024) · Clinical Medicine Insights Endocrinology and Diabetes · doi:10.1177/11795514241235514 · Cited by 14

AI and IoT in Healthcare

Singla, S. K. (2020) · Springer eBooks · doi:10.1007/978-3-030-37526-3_1 · Cited by 14

Wie profitieren Menschen mit Diabetes von Big Data und künstlicher Intelligenz?

Kulzer, B. (2021) · Der Diabetologe · doi:10.1007/s11428-021-00818-9 · Cited by 14

Use of artificial intelligence for management and identification of complications in diabetes

Behera, A. (2021) · Clinical Diabetology · doi:10.5603/dk.a2021.0007 · Cited by 12

Role of Artificial Intelligence in Diabetic Wound Screening and Early Detection

Dhankhar, S., Garg, N., Chauhan, S., & Saini, M. (2024) · Current Biotechnology · doi:10.2174/0122115501303253240408072559 · Cited by 11

Artificial Intelligence as a Tool for Self-Care in Patients with Type 1 and Type 2 Diabetes—An Integrative Literature Review

Persson, V., & Wickman, U. L. (2025) · Healthcare · doi:10.3390/healthcare13080950 · Cited by 11

Data Mining Electronic Health Records to Support Evidence-Based Clinical Decisions

Magboo, M. S. A., & Coronel, A. D. (2019) · Smart innovation, systems and technologies · doi:10.1007/978-981-13-8566-7_22 · Cited by 11

AIoMT artificial intelligence (AI) and Internet of Medical Things (IoMT)

Muheidat, F., & Tawalbeh, L. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-99421-7.00013-1 · Cited by 12

AI applications in prevalent diseases and disorders

Catania, L. J. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-824477-7.00007-9 · Cited by 11

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

Sikalidis, A. K., Kristo, A. S., Reaves, S. K., Kurfeß, F. J., DeLay, A. M., Vasilaky, K., & Donegan, L. (2022) · Sensors · doi:10.3390/s22218299 · Cited by 11

Development of a Novel Insulin Sensor for Clinical Decision-Making

Vargas, E., Aiello, E. M., Pinsker, J. E., Teymourian, H., Tehrani, F., Church, M. M., Laffel, L. M., Doyle, F. J., Patti, M., Dassau, E., et al. (2022) · Journal of Diabetes Science and Technology · doi:10.1177/19322968211071132 · Cited by 10

Enabling the efficiency of Blockchain Technology in Tele-Healthcare with Enhanced EMR

Sheela, K. S., & Priya, C. (2020) · doi:10.1109/iccsea49143.2020.9132922 · Cited by 9

A Scoping Review of Artificial Intelligence-Based Health Education Interventions for Patients with Type 2 Diabetes

Li, C., Li, W., Shao, Y., Xu, Z., Song, J., & Wang, Y. (2025) · Diabetes Metabolic Syndrome and Obesity · doi:10.2147/dmso.s541515 · Cited by 8

Strategy of Artificial Intelligence Assisted Health Tourism in the Perspective of Global Region Based on Markov Chain Model

Chen, P. (2020) · International Conference on Computing Methodologies and Communication (ICCMC) · doi:10.1109/iccmc48092.2020.iccmc-00073 · Cited by 7

Using Real-Time, Partially Automated Interactive System to Interpret Patient’s Data; Helping The Patient To Achieve Diabetic Self-Management: A Rapid Literature Review

Hani, S. B., & Saleh, M. (2022) · Current Diabetes Reviews · doi:10.2174/1573399819666221031161442 · Cited by 10

Developing Clinical Decision Support System using Machine Learning Methods for Type 2 Diabetes Drug Management

Singla, R., Aggarwal, S., Bindra, J., Garg, A., & Singla, A. (2022) · Indian Journal of Endocrinology and Metabolism · doi:10.4103/ijem.ijem_435_21 · Cited by 8

From Evaluation to Prediction: Analysis of Diabetic Autonomic Neuropathy Using Sudoscan and Artificial Intelligence

Toderean, R., Cobuz, M., Dimian, M., & Cobuz, C. (2024) · Applied Sciences · doi:10.3390/app14167406 · Cited by 7

Artificial Intelligence and Machine Learning for Diabetes Decision Support

Vehı́, J., Mujahid, O., & Contreras, I. (2022) · Springer series on bio- and neurosystems · doi:10.1007/978-3-030-99728-1_13 · Cited by 8

Artificial Intelligence, Machine Learning, and Natural Language Processing

Shoenbill, K. A., Kasthurirathne, S. N., & Mendonça, E. A. (2023) · Springer eBooks · doi:10.1007/978-3-031-29171-5_36 · Cited by 9

Extending Capabilities of Artificial Intelligence for Decision-Making and Healthcare Education

Javaid, M., Haleem, A., Khan, I. H., Vaishya, R., & Vaish, A. (2020) · Apollo Medicine · doi:10.4103/am.am_10_20 · Cited by 7

Precision medicine in diabetes—Finally some light at the end of the tunnel?

Raj, G. M., & Mathaiyan, J. (2020) · British Journal of Clinical Pharmacology · doi:10.1111/bcp.14674 · Cited by 7

Implantation of an Innovative Intracardiac Microcomputer System for Web-Based Real-Time Monitoring of Heart Failure: Usability and Patients’ Attitudes

́Ancona, G. D., Murero, M., Feickert, S., Kaplan, H., Öner, A. Ö., Ortak, J., & Ince, H. (2021) · JMIR Cardio · doi:10.2196/21055 · Cited by 7

Popular Diabetes Mobile Applications for Medication Intake Monitoring

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Artificial Intelligence for Detection of Dementia Using Motion Data: A Scoping Review

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