Abstract

Type 2 diabetes mellitus (T2DM) is a chronic condition marked by insulin resistance and impaired glucose metabolism, leading to significant morbidity and mortality. The integration of artificial intelligence (AI), particularly machine learning (ML), into primary healthcare is enhancing predictive and diagnostic accuracy in T2DM management. This narrative review aims to summarize current AI applications in the prediction and management of T2DM among adults in primary care and to provide evidence-based clinical recommendations for nurse practitioners (NPs) on integrating AI tools into their practice. A search of PubMed, EMBASE, and CINAHL databases was conducted. The inclusion criteria limited studies to English language journal articles involving adults (18+ years old) published between 2019-2024. Both observational and experimental studies were included while study protocols and conference abstracts were excluded. Existing research demonstrates the various ways in which custom predictive T2DM AI models can be developed and used. Such models can predict T2DM disease progression and comorbidities (like hypertension), and aid in the development of personalized treatment plans. Specifically, studies into AI applications in T2DM management, like voice-based conversational AI, have demonstrated AI’s enhancement of self-management of insulin therapy titration and the insights AI can provide into patient’s diabetes self-management activities. Other uses include aiding NPs in clinical decision-making, treatment selection, and remote monitoring of T2DM patients. While remaining cognizant of possible safety and ethical concerns, NPs should stay abreast of AI advancements and the possible implementation of AI technology into diabetic patient care to improve patient outcomes.

Notes

References:

Ansari, R. M., Harris, M. F., Hosseinzadeh, H., & Zwar, N. (2023). Application of artificial intelligence in assessing the self-management practices of patients with type 2 diabetes. Healthcare, 11(6), 903-918. https://doi.org/10.3390/healthcare11060903

Kanagasingam, Y., Xiao, D., Vignarajan, J., Preetham, A., Tay-Kearney, M. L., & Mehrotra, A. (2018). Evaluation of artificial intelligence-based grading of diabetic retinopathy in primary care. JAMA Network Open, 1(5), 1-6. https://doi.org/10.1001/jamanetworkopen.2018.2665

Nagaraj, S. B., Sidorenkov, G., van Boven, J. F. M., & Denig, P. (2019). Predicting short- and long-term glycated haemoglobin response after
insulin initiation in patients with type 2 diabetes mellitus using machine-learning algorithms. Diabetes, Obesity & Metabolism, 21(12), 2704–2711. https://doi.org/10.1111/dom.13860

Nayak, A., Vakili, S., Nayak, K., Nikolov, M., Chiu, M., Sosseinheimer, P., Talamantes, S., Testa, S., Palanisamy, S., Giri, V., & Schulman, K.
(2023). Use of voice-based conversational artificial intelligence for basal insulin prescription management among patients with type 2 diabetes: A randomized clinical trial. JAMA Network Open, 6(12), 1-11. https://doi.org/10.1001/jamanetworkopen.2023.40232

Ozturk, B., Lawton, T., Smith, S., & Habli, I. (2023). Predicting progression of type 2 diabetes using primary care data with the help of machine
learning. Studies in Health Technology and Informatics, 302, 38–42. https://doi.org/10.3233/SHTI230060

Zhang, L., Shang, X., Sreedharan, S., Yan, X., Liu, J., Keel, S., Wu, J., Peng, W., & He, M. (2020). Predicting the development of type 2 diabetes in a large Australian cohort using machine-learning techniques: Longitudinal survey study. JMIR Medical Informatics, 8(7), 1-10. https://doi.org/10.2196/16850

Description

Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder that leads to significant morbidity and mortality. This review aims to summarize current AI applications in predicting and managing T2DM in adult patients in primary care, providing evidence-based recommendations for nurse practitioners (NPs) who are considering implementing AI to improve patient outcomes. Research demonstrates AI's role in predicting T2DM progression, assisting in self-management, and aiding in clinical decisions.

Author Details

Adelina Ailarov, MS, RN; Vicky Chen, MS, RN; Sergine Delma, BSN, RN

Sigma Membership

Alpha Zeta

Type

Poster

Format Type

Text-based Document

Study Design/Type

Other

Research Approach

Qualitative Research

Keywords:

Primary Care, Instrument and Tool Development, Type 2 Diabetes Mellitus, T2DM, Artificial Intelligence, AI

Conference Name

36th International Nursing Research Congress

Conference Host

Sigma Theta Tau International

Conference Location

Seattle, Washington, USA

Conference Year

2025

Rights Holder

All rights reserved by the author(s) and/or publisher(s) listed in this item record unless relinquished in whole or part by a rights notation or a Creative Commons License present in this item record.

Review Type

Abstract Review Only: Reviewed by Event Host

Acquisition

Proxy-submission

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AI Use in the Management of Adults with Type 2 Diabetes Mellitus in Primary Care: A Narrative Review

Seattle, Washington, USA

Type 2 diabetes mellitus (T2DM) is a chronic condition marked by insulin resistance and impaired glucose metabolism, leading to significant morbidity and mortality. The integration of artificial intelligence (AI), particularly machine learning (ML), into primary healthcare is enhancing predictive and diagnostic accuracy in T2DM management. This narrative review aims to summarize current AI applications in the prediction and management of T2DM among adults in primary care and to provide evidence-based clinical recommendations for nurse practitioners (NPs) on integrating AI tools into their practice. A search of PubMed, EMBASE, and CINAHL databases was conducted. The inclusion criteria limited studies to English language journal articles involving adults (18+ years old) published between 2019-2024. Both observational and experimental studies were included while study protocols and conference abstracts were excluded. Existing research demonstrates the various ways in which custom predictive T2DM AI models can be developed and used. Such models can predict T2DM disease progression and comorbidities (like hypertension), and aid in the development of personalized treatment plans. Specifically, studies into AI applications in T2DM management, like voice-based conversational AI, have demonstrated AI’s enhancement of self-management of insulin therapy titration and the insights AI can provide into patient’s diabetes self-management activities. Other uses include aiding NPs in clinical decision-making, treatment selection, and remote monitoring of T2DM patients. While remaining cognizant of possible safety and ethical concerns, NPs should stay abreast of AI advancements and the possible implementation of AI technology into diabetic patient care to improve patient outcomes.