Artificial intelligence/machine learning in diabetes care
Open accessAbstract
Artificial intelligence/Machine learning (AI/ML) is transforming all spheres of our life, including the healthcare system. Application of AI/ML has a potential to vastly enhance the reach of diabetes care thereby making it more efficient. The huge burden of diabetes cases in India represents a unique set of problems, and provides us with a unique opportunity in terms of potential availability of data. Harnessing this data using electronic medical records, by all physicians, can put India at the forefront of research in this area. Application of AI/ML would provide insights to our problems as well as may help us to devise tailor-made solutions for the same.
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Applications of artificial intelligence (machine learning) algorithms have become increasingly popular in many medical domains, but the formulation of such algorithms for modeling the long-term progression of diabetes remains limited.
“Although the applications of artificial intelligence (machine learning) algorithms have been become increasingly popular in manymedical domains, the formulation of such algorithms for modeling the long-term progression of diabetes remains limited (81).”
From The progression of secondary diabetes: A review of modeling studies (2022) · cites this paper as [81]
The discussion preceding TYPE 2 DIABETES MANAGEMENT says AI/ML is used for several diabetes-related tasks, and the TYPE 2 DIABETES MANAGEMENT section says published work on using it to optimize patient-level therapy is scant. Neither passage establishes that algorithms for modeling the long-term progression of diabetes are limited. The paper also describes healthcare adoption of AI/ML as slow, rather than establishing increasing popularity across medical domains; page numbers are unavailable in our copy. (LLM, conf=0.82)
Experimental — accuracy not yet measuredAI plays an important role in predicting complications using basic clinical and biochemical patient data.
“AI plays an important role in predicting complications using basic clinical and biochemical patient data”
From Artificial intelligence based prediction models for individuals at risk of multiple diabetic complications: A systematic review of the literature (2022) · cites this paper as Singla et al., 2019
The section labeled Abstract (page unavailable) says AI/ML is used to predict the risk of diabetes complications, including nephropathy and retinopathy. It does not specify that those predictions use basic clinical and biochemical patient data; the blood-glucose and HbA1c measures discussed under Type 2 Diabetes Management concern treatment decisions, not complication prediction. (LLM, conf=0.86)
Experimental — accuracy not yet measuredPredicting the occurrence of different complications is a challenging task due to different risk factors, unbalanced data, and rapid changes.
“but predicting the occurrence of different complications is a challenging task due to different risk factors, unbalanced data and rapid changes (Singla et al., 2019).”
From Artificial intelligence based prediction models for individuals at risk of multiple diabetic complications: A systematic review of the literature (2022) · cites this paper as Singla et al., 2019
The Abstract (page not specified) says AI/ML is used to predict the risk of complications such as nephropathy and retinopathy. The Type 2 Diabetes Management section discusses noisy data and individual factors in treatment decisions, but neither passage says complication prediction is challenging because of different risk factors, unbalanced data, or rapid changes. (LLM, conf=0.76)
Experimental — accuracy not yet measured
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- Authors
- Singla, R., Singla, A., Gupta, Y., & Kalra, S.
- Affiliations
- Kalpavriksh (IN)
- Corresponding author
- Singla, Rajiv
- Singla, Ankush
- Venue
- Indian Journal of Endocrinology and Metabolism
- Published
- 2019
- Volume
- 23
- Issue
- 4
- Pages
- 495
- ISSN
- 2230-8210 (ISSN-L)
- PMC
- PMC6844177
- OpenAlex
- W2977381217
- Cited by
- 52
- References
- 16
- Citations by year
- 2026: 4 · 2025: 10 · 2024: 9 · 2023: 12 · 2022: 11 · 2021: 2 · 2020: 3 · 2018: 1
- FWCI
- 1.27
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by-nc-sa
- Locations
- Indian Journal of Endocrinology and Metabolism (publishedVersion, cc-by-nc-sa)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-sa)
- PubMed Central (publishedVersion, cc-by-nc-sa)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- artificial intelligence, machine learning, diabetes management, electronic health records, health information use, personalized treatment
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