Overview of Artificial Intelligence–Driven Wearable Devices for Diabetes: Scoping Review
Open accessAbstract
Background: Prevalence of diabetes has steadily increased over the last few decades with 1.5 million deaths reported in 2012 alone. Traditionally, analyzing patients with diabetes has remained a largely invasive approach. Wearable devices (WDs) make use of sensors historically reserved for hospital settings. WDs coupled with artificial intelligence (AI) algorithms show promise to help understand and conclude meaningful information from the gathered data and provide advanced and clinically meaningful analytics. Objective: This review aimed to provide an overview of AI-driven WD features for diabetes and their use in monitoring diabetes-related parameters. Methods: We searched 7 of the most popular bibliographic databases using 3 groups of search terms related to diabetes, WDs, and AI. A 2-stage process was followed for study selection: reading abstracts and titles followed by full-text screening. Two reviewers independently performed study selection and data extraction, and disagreements were resolved by consensus. A narrative approach was used to synthesize the data. Results: From an initial 3872 studies, we report the features from 37 studies post filtering according to our predefined inclusion criteria. Most of the studies targeted type 1 diabetes, type 2 diabetes, or both (21/37, 57%). Many studies (15/37, 41%) reported blood glucose as their main measurement. More than half of the studies (21/37, 57%) had the aim of estimation and prediction of glucose or glucose level monitoring. Over half of the reviewed studies looked at wrist-worn devices. Only 41% of the study devices were commercially available. We observed the use of multiple sensors with photoplethysmography sensors being most prevalent in 32% (12/37) of studies. Studies reported and compared >1 machine learning (ML) model with high levels of accuracy. Support vector machine was the most reported (13/37, 35%), followed by random forest (12/37, 32%). Conclusions: This review is the most extensive work, to date, summarizing WDs that use ML for people with diabetes, and provides research direction to those wanting to further contribute to this emerging field. Given the advancements in WD technologies replacing the need for invasive hospital setting devices, we see great advancement potential in this domain. Further work is needed to validate the ML approaches on clinical data from WDs and provide meaningful analytics that could serve as data gathering, monitoring, prediction, classification, and recommendation devices in the context of diabetes.
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Wearable devices and mobile applications powered by AI have become essential tools for real-time diabetes management.
“Wearable devices and mobile applications powered by AI have become essential tools for real-time diabetes management (37).”
From Generative artificial intelligence in predictive analysis of diabetes and its complications: a narrative review (2025) · cites this paper as [37]
The Introduction says apps can deliver diabetes monitoring data and that AI can analyze data collected continuously by wearables. But it describes noninvasive wearables as being in their infancy, while the Discussion says many wearable studies lack machine learning; it does not establish that AI-powered wearables and apps have become essential tools for real-time diabetes management. (LLM, conf=0.88)
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- Authors
- Ahmed, A., Aziz, S., Abd-alrazaq, A., Farooq, F., & Sheikh, J.
- Affiliations
- Weill Cornell Medical College in Qatar (QA)
- Corresponding author
- Ahmed, Arfan
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2022-08-09
- Volume
- 24
- Issue
- 8
- Pages
- e36010
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC9399882
- OpenAlex
- W4290777035
- Cited by
- 57
- References
- 54
- Citations by year
- 2026: 14 · 2025: 16 · 2024: 16 · 2023: 9 · 2022: 2
- FWCI
- 1.53
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- Funders
- Weill Cornell Medical College
- Weill Cornell Medicine - Qatar
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Keywords
- wearable devices, diabetes mellitus, artificial intelligence, machine learning, blood glucose monitoring, glucose prediction, type 1 diabetes, type 2 diabetes, photoplethysmography, wrist-worn sensors, support vector machine, random forest, non-invasive monitoring
- MeSH terms
- Wearable Electronic Devices, Artificial Intelligence, Blood Glucose, Diabetes Mellitus, Type 1, Diabetes Mellitus, Type 2, Humans
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