Artificial intelligence in diabetes management
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Generative AI can integrate diverse inputs such as metabolic dysfunction, lifestyle habits, and genetic predispositions to provide personalized risk assessments in diabetes care.
“In diabetes care, where factors such as metabolic dysfunction, lifestyle habits, and genetic predispositions all play critical roles, generative AI can integrate these diverse inputs to provide personalized risk assessments (20).”
From Generative artificial intelligence in predictive analysis of diabetes and its complications: a narrative review (2025) · cites this paper as [20]
Generative AI models are transforming the detection of diabetic neuropathy by analyzing complex datasets, including nerve conduction studies, patient-reported symptoms, and genetic data.
“Generative AI models are transforming the detection of diabetic neuropathy by analyzing complex datasets, including nerve conduction studies, patient-reported symptoms, and genetic data (20).”
From Generative artificial intelligence in predictive analysis of diabetes and its complications: a narrative review (2025) · cites this paper as [20]
Cites
Dissecting racial bias in an algorithm used to manage the health of populations
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019) · Science · doi:10.1126/science.aax2342 · Cited by 7,137 · see citing works
Scalable and accurate deep learning with electronic health records
Rajkomar, A., Oren, E., Chen, K., Dai, A. M., Hajaj, N., Hardt, M., Liu, P. J., Liu, X., Marcus, J., Sun, M., et al. (2018) · npj Digital Medicine · doi:10.1038/s41746-018-0029-1 · Cited by 2,648 · see citing works
Sheller, M. J., Edwards, B., Reina, G. A., Martin, J., Pati, S., Kotrotsou, A., Milchenko, M., Xu, W., Marcus, D., Colen, R. R., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-69250-1 · Cited by 1,646 · see citing works
Meal Simulation Model of the Glucose-Insulin System
Dalla Man, C., Rizza, R. A., & Cobelli, C. (2007) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2007.893506 · Cited by 955 · see citing works
Security and privacy in electronic health records: a systematic literature review.
Fernández-Alemán, J. L., Señor, I. C., Lozoya, P. Á. O., & Toval, A. (2013) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2012.12.003 · Cited by 675 · see citing works
Beck, R. W., Riddlesworth, T. D., Ruedy, K., Ahmann, A., Haller, S., Kruger, D., McGill, J. B., Polonsky, W., Price, D., Aronoff, S., et al. (2017) · Annals of Internal Medicine · doi:10.7326/m16-2855 · Cited by 635 · see citing works
Gregg, E. W., Cheng, Y. J., Srinivasan, M., Lin, J., Geiss, L. S., Albright, A. L., & Imperatore, G. (2018) · The Lancet · doi:10.1016/s0140-6736(18)30314-3 · Cited by 480 · see citing works
Forlenza, G. P., Li, Z., Buckingham, B. A., Pinsker, J. E., Cengiz, E., Wadwa, R. P., Ekhlaspour, L., Church, M. M., Weinzimer, S. A., Jost, E., et al. (2018) · Diabetes Care · doi:10.2337/dc18-0771 · Cited by 246 · see citing works
Karter, A. J., Warton, E. M., Lipska, K. J., Ralston, J. D., Moffet, H. H., Jackson, G. G., Huang, E. S., & Miller, D. R. (2017) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2017.3844 · Cited by 150 · see citing works
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Osonuga, A., Olawade, D. B., Gore, M., Rotifa, O. B., Egbon, E., Chinwah, V., & Boussios, S. (2025) · Annals of Translational Medicine · doi:10.21037/atm-25-62 · Cited by 4 · see citing works
- Authors
- Thomas, A., Gopi, V. P., & Francis, B.
- Affiliations
- Vellore Institute of Technology University (IN)
- National Institute of Technology Tiruchirappalli (IN)
- Venue
- Advances in Artificial Intelligence
- Publisher
- Elsevier
- Published
- 2024
- Pages
- 397-436
- OpenAlex
- W4399226961
- Cited by
- 3
- References
- 9
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 1
- FWCI
- 1.27
- Type
- book chapter
- Language
- en
- Open access
- closed
- Licence
- Locations
- Elsevier eBooks (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- artificial intelligence, diabetes management, machine learning, blood glucose monitoring, continuous glucose monitoring, hypoglycemia detection, hyperglycemia detection, personalized diabetes management, insulin dose, decision support system, diabetes complications, health data integration, ethical considerations, privacy and security, algorithmic bias
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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