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Review
. 2025 Jan;19(1):246-264.
doi: 10.1177/19322968241287773. Epub 2024 Nov 22.

Artificial Intelligence to Diagnose Complications of Diabetes

Alessandra T Ayers  1 , Cindy N Ho  1 , David Kerr  2 , Simon Lebech Cichosz  3 , Nestoras Mathioudakis  4 , Michelle Wang  5 , Bijan Najafi  6   7 , Sun-Joon Moon  8 , Ambarish Pandey  9 , David C Klonoff  1   10
Affiliations
Review

Artificial Intelligence to Diagnose Complications of Diabetes

Alessandra T Ayers et al. J Diabetes Sci Technol. 2025 Jan.

Abstract

Artificial intelligence (AI) is increasingly being used to diagnose complications of diabetes. Artificial intelligence is technology that enables computers and machines to simulate human intelligence and solve complicated problems. In this article, we address current and likely future applications for AI to be applied to diabetes and its complications, including pharmacoadherence to therapy, diagnosis of hypoglycemia, diabetic eye disease, diabetic kidney diseases, diabetic neuropathy, diabetic foot ulcers, and heart failure in diabetes.Artificial intelligence is advantageous because it can handle large and complex datasets from a variety of sources. With each additional type of data incorporated into a clinical picture of a patient, the calculation becomes increasingly complex and specific. Artificial intelligence is the foundation of emerging medical technologies; it will power the future of diagnosing diabetes complications.

Keywords: artificial intelligence; complications; diabetes; diagnosis; machine learning.

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Conflict of interest statement

Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: ATA is a consultant for Liom. CNH is a consultant for Liom. DK has received research support from Abbott Diabetes Care and consultancy fees from Perfood. SLC has received research support from i-SENS, Inc. NM has nothing to disclose. MW has nothing to disclose. BN is serving as a consultant for BioSensics LLC and Mölnlycke Health on projects unrelated to the scope of this paper. S-JM is a consultant for Abbott, Curestream, Daweoong, EOFlow, G2e, iSense, Medtronic, Novo Nordisk, and Sanofi. AP has received grant funding (to the institution) from Applied Therapeutics, Gilead Sciences, Ultromics, Myovista, and Roche; has served as a consultant for and/or received honoraria outside of the present study as an advisor/consultant for Tricog Health Inc, Lilly USA, Rivus; Cytokinetics, Roche Diagnostics, Sarfez Therapeutics, Edwards Lifesciences, Merck, Bayer, Novo Nordisk, Alleviant, Axon Therapies, and has received nonfinancial support from Pfizer and Merck. AP is also a consultant for Palomarin Inc with stocks compensation and has received research support from the National Institute on Aging GEMSSTAR Grant (1R03AG067960-01), the National Institute on Minority Health and Disparities (R01MD017529), and the National Institute of Heart, Lung, and Blood Institute (R21HL169708). DCK is a consultant for Afon, Embecta, Glucotrack, Lifecare, Novo, Samsung, Synchneuro and Thirdwayv.

Figures

Figure 1.
Figure 1.
Frequently employed types of algorithms for AI. This figure illustrates a variety of algorithms frequently employed in the field of AI, categorized by their type and purpose (supervised, unsupervised, reinforcement, feature reduction, generalization).
Figure 2.
Figure 2.
Conceptualizing compassion. Reproduced from Morrow et al under the CC-BY license (https://creativecommons.org/licenses/by/4.0/).
Figure 3.
Figure 3.
Key findings in AI/ML-based prediction models for hypoglycemia from 70 studies reporting sufficient performance metrics. These findings include a scenario surrounding hypoglycemia (a), whether T1D, T2D, or both were studied (b), sample size (c), length of prediction horizon (d), data inputs (e), ML technique (f), internal validation model performance (g), and external validation model performance (h). Abbreviations: T1D, type 1 diabetes; T2D, type 2 diabetes; Sample size, number of participants; Real-time, <30 minutes; Short-term, 30 to 120 minutes; Adv., advanced; CGM, continuous glucose monitoring, Carb. Int., carbohydrate intake; Clin. Notes, clinic notes; Demogr., demography; Dx, diagnoses; Proc., procedures; Labs, laboratories; Meds, medications; Phys. Act., physical activity; Hypo, hypoglycemia; SMBG, self-monitored blood glucose; Hx, history; Utiliz./Ins., utilization/insurance; Anthr., anthropomorthic; NLP, natural language processing; SVM, support vector machines.
Figure 4.
Figure 4.
Overall schematic diagram describing the practical application of AI in all common ophthalmic imaging modalities. Figure reproduced from Li et al under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Figure 5.
Figure 5.
Summary of current and future uses of AI to diagnose kidney disease as a complication of diabetes. Abbreviations: AI, artificial intelligence; CKD, chronic kidney disease; DKD, diabetic kidney disease.
Figure 6.
Figure 6.
Corneal confocal microscopy showing the sub-basal nerve plexus. (a) Normal structure corneal nerve fibers in a healthy subject. (b) Loss of corneal nerve fibers in a recently diagnosed subject with type 2 diabetes. Figure and caption reformatted from Papanas and Ziegler under the CC BY-NC license 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).
Figure 7.
Figure 7.
AI in DFU. The evidence supporting the benefits of AI in enhancing the management of diabetic foot ulcers remains limited. However, recent studies offer promising results in several areas. These include the use of AI to automate wound classification, provide comprehensive insights into potential risk factors, aid in decision-making,, predict major adverse events such as the risk of major amputation, and potentially facilitate remote triaging of hard-to-heal wounds. This is achieved through the use of digital biomarker surrogates, such as frailty, to assess wound complexity. Component of figure adapted from Xie et al under the CC-BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/). Component of figure adapted from Crepaldi et al under the CC-BY license (https://creativecommons.org/licenses/by/4.0/). Abbreviations: AI, artificial intelligence; DFU, diabetic foot ulcer.
See this image and copyright information in PMC

References

    1. Ahmad E, Lim S, Lamptey R, Webb DR, Davies MJ. Type 2 diabetes. Lancet. 2022;400(10365):1803-1820. doi: 10.1016/S0140-6736(22)01655-5. - DOI - PubMed
    1. Cichosz SL, Johansen MD, Hejlesen O. Toward big data analytics: review of predictive models in management of diabetes and its complications. J Diabetes Sci Technol. 2016;10(1):27-34. doi: 10.1177/1932296815611680. - DOI - PMC - PubMed
    1. Bora A, Balasubramanian S, Babenko B, et al. Predicting the risk of developing diabetic retinopathy using deep learning. Lancet Digit Health. 2021;3(1):e10-e19. doi: 10.1016/S2589-7500(20)30250-8. - DOI - PubMed
    1. Lebech Cichosz S, Hasselstrøm Jensen M, Schou Olesen S. Development and validation of a machine learning model to predict weekly risk of hypoglycemia in patients with type 1 diabetes based on continuous glucose monitoring. Diabetes Technol Ther. 2024;26(7):457-466. doi: 10.1089/dia.2023.0532. - DOI - PubMed
    1. Sidey-Gibbons JAM, Sidey-Gibbons CJ. Machine learning in medicine: a practical introduction. BMC Med Res Methodol. 2019;19(1):64. doi: 10.1186/s12874-019-0681-4. - DOI - PMC - PubMed