Artificial intelligence for diabetes management and decision support: Literature review
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Abstract
BACKGROUND: Artificial intelligence methods in combination with the latest technologies, including medical devices, mobile computing, and sensor technologies, have the potential to enable the creation and delivery of better management services to deal with chronic diseases. One of the most lethal and prevalent chronic diseases is diabetes mellitus, which is characterized by dysfunction of glucose homeostasis. OBJECTIVE: The objective of this paper is to review recent efforts to use artificial intelligence techniques to assist in the management of diabetes, along with the associated challenges. METHODS: A review of the literature was conducted using PubMed and related bibliographic resources. Analyses of the literature from 2010 to 2018 yielded 1849 pertinent articles, of which we selected 141 for detailed review. RESULTS: We propose a functional taxonomy for diabetes management and artificial intelligence. Additionally, a detailed analysis of each subject category was performed using related key outcomes. This approach revealed that the experiments and studies reviewed yielded encouraging results. CONCLUSIONS: We obtained evidence of an acceleration of research activity aimed at developing artificial intelligence-powered tools for prediction and prevention of complications associated with diabetes. Our results indicate that artificial intelligence methods are being progressively established as suitable for use in clinical daily practice, as well as for the self-management of diabetes. Consequently, these methods provide powerful tools for improving patients' quality of life.
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Prior AI models in diabetes management offered several advantages, such as data analysis, pattern recognition, decision support, and patient education, but faced limitations in natural language understanding, personalized treatment, accuracy, and potential biases.
“Prior AI models in diabetes management offered several advantages, such as data analysis, pattern recognition, decision support, and patient education, but faced limitations in natural language understanding, personalized treatment, accuracy, and potential biases.71”
From Artificial intelligence in diabetes management: Advancements, opportunities, and challenges (2023) · cites this paper as [71]
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Experimental — accuracy not yet measuredThe prediction horizon range previously explored ranged between 5 and 180 min, with short-term predictions most frequently explored (most studies used prediction horizons below 60 min).
“The prediction horizon range previously explored ranged between 5 and 180 min. Short-term predictions were the most frequently explored, as most studies used prediction horizons below 60 min.71”
From Artificial intelligence in diabetes management: Advancements, opportunities, and challenges (2023) · cites this paper as [71]
The large amount of data acquired from patients with diabetes makes artificial intelligence techniques particularly attractive for developing diabetes decision support systems.
“The large amount of data that can be acquired from patients with diabetes makes artificial intelligence (AI) techniques particularly attractive for developing diabetes DSSs [25].”
From Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors (2020) · cites this paper as [25]
AI techniques, especially supervised machine learning techniques, are extensively used for building black-box models for glucose prediction, and neural networks are the most used method.
“As reported in recent systematic reviews [25,54], AI techniques, especially supervised machine learning techniques, are extensively used for building black-box models for glucose prediction. The most used method is NN, but other machine learning methodologies, such as random forest, support vector regression, regression algorithms and deep-learning techniques.”
From Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors (2020) · cites this paper as [25]
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Flagged first, then by citations
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Artificial intelligence-enabled healthcare delivery
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Evaluating XAI: A comparison of rule-based and example-based explanations
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Artificial intelligence in diabetes management: Advancements, opportunities, and challenges
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“71 However, large language models (LLMs), which could accept image and text inputs and produce text outputs, have shown promise in various aspects 145 of medical care.”
BackgroundMethodApplications of Machine Learning in Real-Life Digital Health Interventions: Review of the Literature
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Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors
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“Chronic hyperglycemia can cause vascular damage; affect the heart, kidneys, eyes, and nerves; and lead to other serious complications [7,8].”
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“Studies show that AI methods are gradually being established as suitable for diabetes self-management (Contreras & Vehi, 2018).”
MethodHypoglycaemia and its management in primary care setting
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Designing an AI Chatbot for Team-Based Diabetes Care: An Iterative Human-in-the-Loop Approach
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- Authors
- Contreras, I., & Vehi, J.
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2018-05-30
- Volume
- 20
- Issue
- 5
- Pages
- e10775
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC6000484
- OpenAlex
- W2804354698
- Cited by
- 560
- References
- 146
- Citations by year
- 2026: 66 · 2025: 81 · 2024: 76 · 2023: 84 · 2022: 76 · 2021: 67 · 2020: 62 · 2019: 31 · 2018: 6 · 2016: 6 · 2015: 4 · 2014: 1
- FWCI
- 7.69
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Universitat de Girona Digital Repository (Universitat de Girona) (acceptedVersion, cc-by)
- LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (submittedVersion, cc-by)
- RECERCAT (Consorci de Serveis Universitaris de Catalunya) (submittedVersion, other-oa)
- Funders
- European Commission
- European Regional Development Fund
- 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 mellitus, diabetes management, clinical decision support, diabetes self-management, diabetes prevention, glucose homeostasis, chronic disease management, functional classification, quality of life
- MeSH terms
- Machine Learning, Artificial Intelligence, Chronic Disease, Decision Support Techniques, Diabetes Mellitus, Humans, Quality of Life
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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.
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