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Transforming Diabetes Care Through Artificial Intelligence: The Future Is Here
Open accessAbstract
An estimated 425 million people globally have diabetes, accounting for 12% of the world's health expenditures, and yet 1 in 2 persons remain undiagnosed and untreated. Applications of artificial intelligence (AI) and cognitive computing offer promise in diabetes care. The purpose of this article is to better understand what AI advances may be relevant today to persons with diabetes (PWDs), their clinicians, family, and caregivers. The authors conducted a predefined, online PubMed search of publicly available sources of information from 2009 onward using the search terms “diabetes” and “artificial intelligence.” The study included clinically-relevant, high-impact articles, and excluded articles whose purpose was technical in nature. A total of 450 published diabetes and AI articles met the inclusion criteria. The studies represent a diverse and complex set of innovative approaches that aim to transform diabetes care in 4 main areas: automated retinal screening, clinical decision support, predictive population risk stratification, and patient self-management tools. Many of these new AI-powered retinal imaging systems, predictive modeling programs, glucose sensors, insulin pumps, smartphone applications, and other decision-support aids are on the market today with more on the way. AI applications have the potential to transform diabetes care and help millions of PWDs to achieve better blood glucose control, reduce hypoglycemic episodes, and reduce diabetes comorbidities and complications. AI applications offer greater accuracy, efficiency, ease of use, and satisfaction for PWDs, their clinicians, family, and caregivers.
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“AI models have already begun to revolutionize how new drugs are identified and developed, and their applications in diabetes therapeutics are particularly promising (114).”
From Generative artificial intelligence in predictive analysis of diabetes and its complications: a narrative review (2025) · cites this paper as [114]
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“On the other hand, several studies have shown improved diagnostic accuracy, patient triage, and clerical burden reduction.10,11”
From Clinician Adoption of an Artificial Intelligence Algorithm to Detect Left Ventricular Systolic Dysfunction in Primary Care. (2022) · cites this paper as [10]
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Kondepudi, G. M., Pothula, V. S., & Sivaji, K. (2025) · Information systems engineering and management · doi:10.1007/978-3-031-98897-4_9
Fasterholdt, I., Naghavi‐Behzad, M., Rasmussen, B. S. B., Kjølhede, T., Skjøth, M. M., Hildebrandt, M. G., & Kidholm, K. (2022) · Figshare · doi:10.6084/m9.figshare.21443630
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Metadata
- Authors
- Dankwa-Mullan, I., Rivo, M., Sepulveda, M., Park, Y., Snowdon, J., & Rhee, K.
- Affiliations
- IBM Corporation, Watson Health, Bethesda, Maryland.
- Population Health Innovations, Inc., Miami Beach, Florida.
- University of Iowa (US)
- IBM Research - Cambridge (United States) (US)
- IBM (United States) (US)
- Corresponding author
- Rivo, Marc
- Venue
- Population Health Management
- Publisher
- SAGE Publications
- Published
- 2019-05-30
- Volume
- 22
- Issue
- 3
- Pages
- 229-242
- ISSN
- 1942-7891 (ISSN-L)
- 1942-7905
- PMC
- PMC6555175
- OpenAlex
- W2893457521
- Cited by
- 217
- References
- 39
- Citations by year
- 2026: 19 · 2025: 32 · 2024: 38 · 2023: 35 · 2022: 32 · 2021: 20 · 2020: 32 · 2019: 7
- FWCI
- 4.05
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Population Health Management (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (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, automated retinal screening, clinical decision support, diabetes risk stratification, predictive modeling, patient self-management, glucose monitoring, insulin pump, mobile application, glycemic control, hypoglycemia, diabetes complications, cognitive computing
- MeSH terms
- Self-Management, Artificial Intelligence, Decision Support Techniques, Diabetes Mellitus, Forecasting, Humans, Retina, Risk Assessment
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