Transforming Diabetes Care Through Artificial Intelligence: The Future Is Here

A new mathematical model for relative quantification in real-time RT-PCR

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The Effect of Intensive Treatment of Diabetes on the Development and Progression of Long-Term Complications in Insulin-Dependent Diabetes Mellitus

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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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Big data analytics in healthcare: promise and potential

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Home Use of an Artificial Beta Cell in Type 1 Diabetes

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Electronic Health Record Adoption In US Hospitals: Progress Continues, But Challenges Persist

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Cost-Utility Analysis of Screening Intervals for Diabetic Retinopathy in Patients With Type 2 Diabetes Mellitus

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Principles of Health Interoperability HL7 and SNOMED

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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

Keel, S., Lee, P. Y., Scheetz, J., Li, Z., Kotowicz, M. A., MacIsaac, R. J., & He, M. (2018) · Scientific Reports · doi:10.1038/s41598-018-22612-2 · Cited by 220 · see citing works

Retinal Lesion Detection With Deep Learning Using Image Patches

Lam, C., Yu, C., Huang, L. C., & Rubin, D. L. (2018) · Investigative Ophthalmology & Visual Science · doi:10.1167/iovs.17-22721 · Cited by 210 · see citing works

Area Determination of Diabetic Foot Ulcer Images Using a Cascaded Two-Stage SVM-Based Classification

Wang, L., Pedersen, P. C., Agu, E., Strong, D. M., & Tulu, B. (2016) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2016.2632522 · Cited by 177 · see citing works

Artificial Pancreas Device Systems for the Closed-Loop Control of Type 1 Diabetes

Trevitt, S., Simpson, S., & Wood, A. L. (2015) · Journal of Diabetes Science and Technology · doi:10.1177/1932296815617968 · Cited by 175 · see citing works

Smartphone-Based Wound Assessment System for Patients With Diabetes

Wang, L., Pedersen, P. C., Strong, D. M., Tulu, B., Agu, E., & Ignotz, R. A. (2014) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2014.2358632 · Cited by 142 · see citing works

Development of a web-based liver cancer prediction model for type II diabetes patients by using an artificial neural network

Rau, H.-H., Hsu, C., Lin, Y.-A., Atique, S., Fuad, A., Wei, L., & Hsu, M. (2015) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2015.11.009 · Cited by 139 · see citing works

Rule Extraction From Support Vector Machines Using Ensemble Learning Approach: An Application for Diagnosis of Diabetes

Han, L., Luo, S., Yu, J., Pan, L., & Chen, S. (2014) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2014.2325615 · Cited by 133 · see citing works

“Snap-n-Eat”

Zhang, W., Yu, Q., Siddiquie, B., Divakaran, A., & Sawhney, H. (2015) · Journal of Diabetes Science and Technology · doi:10.1177/1932296815582222 · Cited by 132 · see citing works

Bedside Computer Vision — Moving Artificial Intelligence from Driver Assistance to Patient Safety

Yeung, S. Y., Downing, N. L., Fei-Fei, L., & Milstein, A. S. (2018) · New England Journal of Medicine · doi:10.1056/nejmp1716891 · Cited by 118 · see citing works

Gestational Diabetes Management Using Smart Mobile Telemedicine

Rigla, M., Martínez-Sarriegui, I., García-Sáez, G., Pons, B., & Hernando, M. E. (2017) · Journal of Diabetes Science and Technology · doi:10.1177/1932296817704442 · Cited by 92 · see citing works

Advanced Insulin Bolus Advisor based on Run-To-Run Control and Case-Based Reasoning

Herrero, P., Pesl, P., Reddy, M., Oliver, N. S., Georgiou, P., & Toumazou, C. (2014) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2014.2331896 · Cited by 90 · see citing works

Development and Validation of an Algorithm to Identify Nonalcoholic Fatty Liver Disease in the Electronic Medical Record

Corey, K. E., Kartoun, U., Zheng, H., & Shaw, S. Y. (2015) · Digestive Diseases and Sciences · doi:10.1007/s10620-015-3952-x · Cited by 80 · see citing works

Using Machine Learning to Examine Medication Adherence Thresholds and Risk of Hospitalization

Lo‐Ciganic, W., Donohue, J. M., Thorpe, J. M., Perera, S., Thorpe, C. T., Marcum, Z. A., & Gellad, W. F. (2015) · Medical Care · doi:10.1097/mlr.0000000000000394 · Cited by 79 · see citing works

Single Nucleotide Polymorphism relevance learning with Random Forests for Type 2 diabetes risk prediction

López, B., Torrent‐Fontbona, F., Viñas, R., & Fernández‐Real, J. M. (2017) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2017.09.005 · Cited by 75 · see citing works

IBM Watson Analytics: automating visualization, descriptive, and predictive statistics

Hoyt, R. E., Snider, D., Thompson, C., & Mantravadi, S. (2016) · JMIR Public Health and Surveillance · doi:10.2196/publichealth.5810 · Cited by 73 · see citing works

Automated Detection and Grading of Diabetic Maculopathy in Digital Retinal Images

Tariq, A., Akram, M. U., Shaukat, A., & Khan, S. A. (2013) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-012-9549-4 · Cited by 68 · see citing works

Tele-ophthalmology: Opportunities for improving diabetes eye care in resource- and specialist-limited Sub-Saharan African countries

Matimba, A., Woodward, R., Tambo, E., Ramsay, M., Gwanzura, L., & Guramatunhu, S. (2016) · Journal of Telemedicine and Telecare · doi:10.1177/1357633x15604083 · Cited by 53 · see citing works

Fuzzy expert system for diagnosing diabetic neuropathy

Katigari, M. R., Ayatollahi, H., Malek, M., & Haghighi, M. (2017) · World Journal of Diabetes · doi:10.4239/wjd.v8.i2.80 · Cited by 46 · see citing works

Natural occurrence of nocturnal hypoglycemia detection using hybrid particle swarm optimized fuzzy reasoning model

Ling, S. H., & Nguyen, H. T. (2012) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2012.04.003 · Cited by 36 · see citing works

An extensive analysis of various texture feature extractors to detect Diabetes Mellitus using facial specific regions

Shu, T., Zhang, B., & Tang, Y. Y. (2017) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2017.02.005 · Cited by 34 · see citing works

A Soft Computing Approach to Kidney Diseases Evaluation

Neves, J., Martins, M. R., Vilhena, J., Neves, J., Gomes, S., Abelha, A., Machado, J., & Vicente, H. (. (2015) · Journal of Medical Systems · doi:10.1007/s10916-015-0313-4 · Cited by 34 · see citing works

Activity Recognition for Diabetic Patients Using a Smartphone

Cvetković, B., Janko, V., Romero, A. E., Kafalı, Ö., Stathis, K., & Luštrek, M. (2016) · Journal of medical systems · doi:10.1007/s10916-016-0598-y · Cited by 33 · see citing works

A high throughput semantic concept frequency based approach for patient identification: a case study using type 2 diabetes mellitus clinical notes.

Wei, W., Tao, C., Jiang, G., & Chute, C. G. (2010) · PubMed Central · Cited by 33 · see citing works

Stress Testing of an Artificial Pancreas System With Pizza and Exercise Leads to Improvements in the System’s Fuzzy Logic Controller

Mauseth, R. S., Lord, S. M., Hirsch, I. B., Kircher, R. C., Matheson, D. P., & Greenbaum, C. J. (2015) · Journal of Diabetes Science and Technology · doi:10.1177/1932296815602098 · Cited by 32 · see citing works

In Silico Testing of an Artificial-Intelligence-Based Artificial Pancreas Designed for Use in the Intensive Care Unit Setting

DeJournett, L., & DeJournett, J. (2016) · Journal of Diabetes Science and Technology · doi:10.1177/1932296816653967 · Cited by 31 · see citing works

Building and analysis of protein-protein interactions related to diabetes mellitus using support vector machine, biomedical text mining and network analysis

Vyas, R., Bapat, S., Jain, E., Karthikeyan, M., Tambe, S. S., & Kulkarni, B. D. (2016) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2016.09.011 · Cited by 31 · see citing works

Automatic Screening and Classification of Diabetic Retinopathy Fundus Images

Rahim, S. S., Palade, V., Shuttleworth, J., & Jayne, C. (2014) · Communications in computer and information science · doi:10.1007/978-3-319-11071-4_11 · Cited by 28 · see citing works

The burden of severe hypoglycemia in type 2 diabetes

Liu, J., Wang, R., Ganz, M. L., Paprocki, Y., Schneider, D., & Weatherall, J. (2017) · Current Medical Research and Opinion · doi:10.1080/03007995.2017.1391080 · Cited by 27 · see citing works

Computational Intelligence-Based Diagnosis Tool for the Detection of Prediabetes and Type 2 Diabetes in India

Shankaracharya, N. A., Odedra, D., Samanta, S., & Vidyarthi, A. S. (2012) · The Review of Diabetic Studies · doi:10.1900/rds.2012.9.55 · Cited by 23 · see citing works

Using Individualized Predictive Disease Modeling to Identify Patients with the Potential to Benefit from a Disease Management Program for Diabetes Mellitus

Weber, C., & Neeser, K. (2006) · Disease Management · doi:10.1089/dis.2006.9.242 · Cited by 22 · see citing works

Special section on artificial intelligence for diabetes

López, B., Martin, C. E., & Herrero, P. (2017) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2017.09.008 · Cited by 10 · see citing works

Artificial Intelligence: The Future for Diabetes Care

Ellahham, S. H. (2020) · The American Journal of Medicine · doi:10.1016/j.amjmed.2020.03.033 · Cited by 379 · see citing works

Deep Learning for Diabetes: A Systematic Review

Zhu, T., Li, K., Herrero, P., & Georgiou, P. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.3040225 · Cited by 275 · see citing works

Prediction of Type 2 Diabetes Based on Machine Learning Algorithm

Deberneh, H. M., & Kim, I. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18063317 · Cited by 261 · see citing works

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

Makino, M., Yoshimoto, R., Ono, M., Itoko, T., Katsuki, T., Koseki, A., Kudo, M., Haida, K., Kuroda, J., Yanagiya, R., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-48263-5 · Cited by 250 · see citing works

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

Mahmood, T., Li, J., Pei, Y., Akhtar, F., Imran, A., & Rehman, K. U. (2020) · IEEE Access · doi:10.1109/access.2020.3021343 · Cited by 187 · see citing works

Artificial Intelligence in Health Care: Current Applications and Issues

Park, C.-W., Seo, S. W., Kang, N., Ko, B., Choi, B. W., Park, C. M., Chang, D. K., Kim, H., Kim, H., Lee, H., et al. (2020) · Journal of Korean Medical Science · doi:10.3346/jkms.2020.35.e379 · Cited by 171 · see citing works

Artificial intelligence in cardiovascular diseases: diagnostic and therapeutic perspectives

Sun, X., Yin, Y., Yang, Q., & Huo, T. (2023) · European journal of medical research · doi:10.1186/s40001-023-01065-y · Cited by 152 · see citing works

Machine Learning and Smart Devices for Diabetes Management: Systematic Review

Makroum, M. A., Adda, M., Bouzouane, A., & Ibrahim, H. (2022) · Sensors · doi:10.3390/s22051843 · Cited by 145 · see citing works

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

Mansour, M., Darweesh, M. S., & Soltan, A. (2024) · Alexandria Engineering Journal · doi:10.1016/j.aej.2024.01.021 · Cited by 155 · see citing works

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 · see citing works

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

Hassan, A., & Khan, T. (2020) · IEEE Access · doi:10.1109/access.2020.3035026 · Cited by 91 · see citing works

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 · see citing works

Remote patient monitoring using artificial intelligence

Jeddi, Z., & Bohr, A. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-818438-7.00009-5 · Cited by 86 · see citing works

Commercial and Scientific Solutions for Blood Glucose Monitoring—A Review

Xue, Y., Thalmayer, A. S., Zeising, S., Fischer, G., & Lübke, M. (2022) · Sensors · doi:10.3390/s22020425 · Cited by 85 · see citing works

Clinical Opportunities for Continuous Biosensing and Closed-Loop Therapies

Li, J., Liang, J., Laken, S. J., Langer, R., & Traverso, G. (2020) · Trends in Chemistry · doi:10.1016/j.trechm.2020.02.009 · Cited by 73 · see citing works

Artificial Intelligence for Internet of Things and Enhanced Medical Systems

Oniani, S., Marques, G., Barnovi, S., Pires, I. M., & Bhoi, A. K. (2020) · Studies in computational intelligence · doi:10.1007/978-981-15-5495-7_3 · Cited by 73 · see citing works

Deep Physiological Model for Blood Glucose Prediction in T1DM Patients

Muñoz-Organero, M. (2020) · Sensors · doi:10.3390/s20143896 · Cited by 73 · see citing works

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

Luo, Z., Fabre, G., & Rodwin, V. G. (2019) · International Journal of Health Policy and Management · doi:10.15171/ijhpm.2019.80 · Cited by 65 · see citing works

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

Chen, T., Wu, H., & Chiu, M.-C. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.111183 · Cited by 65 · see citing works

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 63 · see citing works

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

Shen, J., Chen, J., Zheng, Z., Zheng, J., Liu, Z., Song, J., Wong, S. Y., Wang, X., Huang, M., Fang, P.-H., et al. (2020) · Journal of Medical Internet Research · doi:10.2196/21573 · Cited by 65 · see citing works

Advancing Artificial Intelligence in Health Settings Outside the Hospital and Clinic

Aggarwal, N., Ahmed, M. K., Basu, S., Curtin, J. J., Evans, B. J., Matheny, M. E., Nundy, S., Sendak, M., Shachar, C., Shah, R. U., et al. (2020) · NAM Perspectives · doi:10.31478/202011f · Cited by 63 · see citing works

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

Clark, C. R., Wilkins, C. H., Rodriguez, J. A., Preininger, A. M., Harris, J. W., DesAutels, S. J., Karunakaram, H., Rhee, K. B. L., Bates, D. W., & Dankwa‐Mullan, I. (2021) · Journal of General Internal Medicine · doi:10.1007/s11606-021-06846-x · Cited by 58 · see citing works

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 61 · see citing works

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 · see citing works

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

Gupta, S. K., Lakshmi, P. V. M., Kaur, M., & Rastogi, A. (2020) · Journal of Family Medicine and Primary Care · doi:10.4103/jfmpc.jfmpc_1684_20 · Cited by 56 · see citing works

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 59 · see citing works

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 51 · see citing works

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

Agarwal, Y., Jain, M., Sinha, S., & Dhir, S. (2019) · Global Business and Organizational Excellence · doi:10.1002/joe.21981 · Cited by 46 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 39 · see citing works

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 · see citing works

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 40 · see citing works

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 38 · see citing works

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

Vidal‐Alaball, J., Fibla, D. R., Zapata, M. Á., Marin-Gomez, F. X., & Solans, Ó. (2018) · JMIR Research Protocols · doi:10.2196/12539 · Cited by 37 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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

Omisore, O. M., Ojokoh, B. A., Babalola, A. E., Igbe, T., Folajimi, Y., Nie, Z., & Wang, L. (2020) · Future Generation Computer Systems · doi:10.1016/j.future.2020.10.035 · Cited by 31 · see citing works

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 · see citing works

Diagnosis system for cancer disease using a single setting approach

Bhuyan, H. K., Vijayaraj, A., & Ravi, V. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-15478-8 · Cited by 28 · see citing works

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 36 · see citing works

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 · see citing works

Revolutionizing Healthcare: The Impact of AI‐Powered Sensors

Bhamidipaty, V., Bhamidipaty, D. L., Guntoory, I., Bhamidipaty, K. D. P., Iyengar, K. P., Botchu, B., & Botchu, R. (2025) · Wiley eBooks · doi:10.1002/9781394280735.ch18 · Cited by 29 · see citing works

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 31 · see citing works

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

Mirzaei, M., Harismah, K., Soleimani, M., & Mousavi, S. (2020) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2020.1800513 · Cited by 27 · see citing works

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 · see citing works

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 · see citing works

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 32 · see citing works

Hybrid CNN-LSTM for Predicting Diabetes: A Review

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

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 23 · see citing works

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

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

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Artificial Intelligence in Efficient Diabetes Care

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

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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

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

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

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

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Breast cancer image analysis using deep learning techniques – a survey

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

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

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

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AI and IoT in Healthcare

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

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

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Role of Artificial Intelligence in Diabetic Wound Screening and Early Detection

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

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

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

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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

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Development of a Novel Insulin Sensor for Clinical Decision-Making

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

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

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

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

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

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

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Artificial Intelligence and Machine Learning for Diabetes Decision Support

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Artificial Intelligence, Machine Learning, and Natural Language Processing

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

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

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

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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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Systematic review of Artificial Intelligence-based methods for glycemic control and risk prediction in intensive care units

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Artificial Intelligence for Diabetic Care

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Development and validation of machine learning-augmented algorithm for insulin sensitivity assessment in the community and primary care settings: a population-based study in China

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The association between population health management tools and clinician burnout in the United States VA primary care patient-centered medical home

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Impact of a “Chart Closure” Hard Stop Alert on Prescribing for Elevated Blood Pressures Among Patients With Diabetes: Quasi-Experimental Study

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Health Information Technology as Premise for Data Science in Global Health: A Discussion of Opportunities and Challenges

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Patient Perceptions toward the Application of Artificial Intelligence in Diabetes Care: A Qualitative Study

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Role of Artificial Intelligence in Diabetes Mellitus Care: A SWOT Analysis

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Artificial Intelligence In Diabetes Management: Current Applications And Future Opportunities

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Role of Artificial Intelligence in Diabetes Care: Present Uses, Limitations, and Future Directions

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Artificial Intelligence in Diabetes Care: Applications, Challenges, and Opportunities Ahead

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Revolutionizing diabetes care: the role of artificial intelligence in prevention, diagnosis, and patient care

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Artificial intelligence in diabetes: Clinical applications across the disease continuum

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