The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.
Artificial Intelligence: The Future for Diabetes Care
Abstract
Artificial intelligence is a fast-growing field and its applications to diabetes, a global pandemic, can reform the approach to diagnosis and management of this chronic condition.. Principles of machine learning have been utilized to build algorithms to support predictive models for the risk of developing diabetes or its consequent complications.. Digital therapeutics has proven to be an established intervention for lifestyle therapy in the management of diabetes. Patients are increasingly being empowered for self-management of diabetes and both patients and healthcare professionals are benefitting from clinical decision support. Artificial intelligence allows a continuous and burden-free remote monitoring of the patient's symptoms and biomarkers. Further, social media and online communities enhance patient engagement in diabetes care. Technical advances have helped to optimize resource utilization in diabetes. Together, these intelligent technical reforms have produced better glycemic control with reductions in fasting and postprandial glucose levels, glucose excursions, and glycosylated hemoglobin. Artificial intelligence will introduce a paradigm shift in diabetes care from conventional management strategies to building targeted data-driven precision care.
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Cites
IDF Diabetes Atlas: Global estimates of diabetes prevalence for 2017 and projections for 2045
Cho, N. H., Shaw, J. E., Karuranga, S., Huang, Y., da Rocha Fernandes, J. D., Ohlrogge, A. W., & Malanda, B. (2018) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2018.02.023 · Cited by 7,283
Global Burden of Disease Cancer Collaboration, Fitzmaurice, C., Allen, C., Barber, R. M., Barregard, L., Bhutta, Z. A., Brenner, H., Dicker, D. J., Chimed-Orchir, O., Dandona, R., et al. (2017) · JAMA Oncology · doi:10.1001/jamaoncol.2016.5688 · Cited by 6,215
Global aetiology and epidemiology of type 2 diabetes mellitus and its complications
Zheng, Y., Ley, S. H., & Hu, F. B. (2018) · Nature Reviews Endocrinology · doi:10.1038/nrendo.2017.151 · Cited by 5,697
Mahajan, A., Taliun, D., Thurner, M., Robertson, N. R., Torres, J. M., Rayner, N. W., Payne, A. J., Steinthorsdottir, V., Scott, R. A., Grarup, N., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0241-6 · Cited by 1,744
A Holistic Framework to Improve the Uptake and Impact of eHealth Technologies
van Gemert-Pijnen, J. E., Nijland, N., van Limburg, M., Ossebaard, H. C., Kelders, S. M., Eysenbach, G., & Seydel, E. R. (2011) · Journal of Medical Internet Research · doi:10.2196/jmir.1672 · Cited by 1,126
Predicting Diabetes Mellitus With Machine Learning Techniques
Zou, Q., Qu, K., Luo, Y., Yin, D., Ju, Y., & Tang, H. (2018) · Frontiers in Genetics · doi:10.3389/fgene.2018.00515 · Cited by 920
Wearable Devices as Facilitators, Not Drivers, of Health Behavior Change
Patel, M. S., Asch, D. A., & Volpp, K. G. (2015) · JAMA · doi:10.1001/jama.2014.14781 · Cited by 808
Design of an mHealth app for the self-management of adolescent type 1 diabetes: a pilot study
Cafazzo, J. A., Casselman, M., Hamming, N., Katzman, D. K., & Palmert, M. R. (2012) · Journal of Medical Internet Research · doi:10.2196/jmir.2058 · Cited by 676
Artificial intelligence for diabetes management and decision support: Literature review
Contreras, I., & Vehi, J. (2018) · Journal of Medical Internet Research · doi:10.2196/10775 · Cited by 560
Hou, C., Carter, B., Hewitt, J., Francisa, T., & Mayor, S. (2016) · Diabetes Care · doi:10.2337/dc16-0346 · Cited by 411
Cited by
Artificial Intelligence in Current Diabetes Management and Prediction
Nomura, A., Noguchi, M., Kometani, M., Furukawa, K., & Yoneda, T. (2021) · Current Diabetes Reports · doi:10.1007/s11892-021-01423-2 · Cited by 183
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
Artificial intelligence for diabetes care: current and future prospects
Sheng, B., Pushpanathan, K., Guan, Z., Lim, Q. H., Lim, Z. W., Yew, S. M. E., Goh, J. H. L., Bee, Y. M., Sabanayagam, C., Sevdalis, N., et al. (2024) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(24)00154-2 · Cited by 106
Medical education and physician training in the era of artificial intelligence
Xu, Y., Jiang, Z., Ting, D. S. W., Kow, A. W. C., Bello, F., Car, J., Tham, Y.-C., & Wong, T. Y. (2024) · Singapore Medical Journal · doi:10.4103/singaporemedj.smj-2023-203 · Cited by 76
Abbas, A., Rehman, M. S., & Rehman, S. S. (2024) · Cureus · doi:10.7759/cureus.55991 · Cited by 58
Metadata
- Authors
- Ellahham, S.
- Venue
- The American Journal of Medicine
- Publisher
- Elsevier BV
- Published
- 2020-08
- Volume
- 133
- Issue
- 8
- Pages
- 895-900
- ISSN
- 0002-9343 (ISSN-L)
- OpenAlex
- W3017000889
- Cited by
- 373
- References
- 42
- FWCI
- 9.77
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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