Artificial Intelligence in Current Diabetes Management and Prediction
Open accessAbstract
Purpose of Review: Artificial intelligence (AI) can make advanced inferences based on a large amount of data. The mainstream technologies of the AI boom in 2021 are machine learning (ML) and deep learning, which have made significant progress due to the increase in computational resources accompanied by the dramatic improvement in computer performance. In this review, we introduce AI/ML-based medical devices and prediction models regarding diabetes. Recent Findings: In the field of diabetes, several AI-/ML-based medical devices and regarding automatic retinal screening, clinical diagnosis support, and patient self-management tool have already been approved by the US Food and Drug Administration. As for new-onset diabetes prediction using ML methods, its performance is not superior to conventional risk stratification models that use statistical approaches so far. Summary: Despite the current situation, it is expected that the predictive performance of AI will soon be maximized by a large amount of organized data and abundant computational resources, which will contribute to a dramatic improvement in the accuracy of disease prediction models for diabetes.
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IDx-DR facilitates DR screening, especially in rural communities where patients have difficulty accessing an ophthalmologist.
“This device facilitates DR screening, especially in rural communities where patients have difficulty accessing an ophthalmologist.143”
From Artificial intelligence in diabetes management: Advancements, opportunities, and challenges (2023) · cites this paper as [143]
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Artificial intelligence in diabetes management: Advancements, opportunities, and challenges
Guan, Z., Li, H., Liu, R., Cai, C., Liu, Y., Li, J., Wang, X., Huang, S., Wu, L., Liu, D., et al. (2023) · Cell Reports Medicine · doi:10.1016/j.xcrm.2023.101213 · Cited by 287 · see citing works
“143 Several DL algorithms with high sensitivity have recently been developed for DR screening, predominantly focusing on identifying referable DR or vision-threatening DR.”
Background
- Authors
- Nomura, A., Noguchi, M., Kometani, M., Furukawa, K., & Yoneda, T.
- Affiliations
- Kanazawa University (JP)
- Corresponding author
- Nomura, Akihiro
- Venue
- Current Diabetes Reports
- Published
- 2021-12
- Volume
- 21
- Issue
- 12
- Pages
- 61-61
- ISSN
- 1534-4827 (ISSN-L)
- 1539-0829
- PMC
- PMC8668843
- OpenAlex
- W4200088206
- Cited by
- 183
- References
- 2
- Citations by year
- 2026: 32 · 2025: 63 · 2024: 48 · 2023: 33 · 2022: 7
- FWCI
- 19.39
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Current Diabetes Reports (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- ministry of health, labour and welfare (20FA0701)
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Keywords
- artificial intelligence, diabetes management, diabetes prediction, machine learning, deep learning, AI-based medical devices, retinal screening, medical diagnosis support, patient self-management, risk stratification model, FDA approval
- MeSH terms
- Machine Learning, Self-Management, Artificial Intelligence, Diabetes Mellitus, Humans, United States
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