Applying Artificial Intelligence-Driven Approaches in Diabetes Care Nursing: Overcoming Challenges to Improve Patient Outcomes

Abstract Artificial intelligence (AI) is transforming nursing care with innovative solutions for managing diabetic patients. Integrating AI allows nurses to improve patient assessment accuracy, personalize care plans, and streamline management, enhancing patient outcomes and care quality. Therefore, this study aims to assess diabetes management through AI, including its limitations, disadvantages, and benefits of integrating AI with patient-centered care (PCC). This review article analyzes existing literature on the integration of AI in nursing care for patients with diabetes, evaluating studies that demonstrate improvements in patient assessment accuracy, personalized care plans, and management efficiency. This study offers insights into how AI contributes to achieving quality nursing management for diabetic patients. However, challenges such as data privacy concerns, high implementation costs, and the need for specialized training hinder widespread adoption. In conclusion, the incorporation of AI into nursing management for diabetes signifies a promising advancement in the healthcare delivery system. Despite the challenges, the potential and research value of AI in nursing practice are undeniable, as its applications continue to expand rapidly. Future diabetic programs that utilize comprehensive and integrated AI technologies promise to deliver improved care for diabetes patients.

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Publication Details

Journal
D Y Patil Journal of Health Sciences
Published
2025-01-01
DOI
https://doi.org/10.4103/dypj.dypj_49_24
Citations
1
Primary Topic
Artificial Intelligence in Healthcare
Type
article
Field-Weighted Citation Impact
2.94
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article

Applying Artificial Intelligence-Driven Approaches in Diabetes Care Nursing: Overcoming Challenges to Improve Patient Outcomes

Olaolorunpo Olorunfemi, O E Ayodele, Rosemary Ngozi Osunde
1 citations
D Y Patil Journal of Health Sciences
Artificial Intelligence in Healthcare
2.94
article

Applying Artificial Intelligence-Driven Approaches in Diabetes Care Nursing: Overcoming Challenges to Improve Patient Outcomes

Olaolorunpo Olorunfemi, O E Ayodele, Rosemary Ngozi Osunde
article en
1 citations

Abstract

Abstract Artificial intelligence (AI) is transforming nursing care with innovative solutions for managing diabetic patients. Integrating AI allows nurses to improve patient assessment accuracy, personalize care plans, and streamline management, enhancing patient outcomes and care quality. Therefore, this study aims to assess diabetes management through AI, including its limitations, disadvantages, and benefits of integrating AI with patient-centered care (PCC). This review article analyzes existing literature on the integration of AI in nursing care for patients with diabetes, evaluating studies that demonstrate improvements in patient assessment accuracy, personalized care plans, and management efficiency. This study offers insights into how AI contributes to achieving quality nursing management for diabetic patients. However, challenges such as data privacy concerns, high implementation costs, and the need for specialized training hinder widespread adoption. In conclusion, the incorporation of AI into nursing management for diabetes signifies a promising advancement in the healthcare delivery system. Despite the challenges, the potential and research value of AI in nursing practice are undeniable, as its applications continue to expand rapidly. Future diabetic programs that utilize comprehensive and integrated AI technologies promise to deliver improved care for diabetes patients.

D Y Patil Journal of Health SciencesVol. 13(1)
Wellspring University (NG), Achievers University (NG)
Openalex Percentile: Top 9%
Artificial Intelligence in Healthcare
2.94
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Applying Artificial Intelligence-Driven Approaches in Diabetes Care Nursing: Overcoming Challenges to Improve Patient Outcomes — Olaolorunpo Olorunfemi, O E Ayodele, et al. · D Y Patil Journal of Health Sciences (2025) | TGRS Research Map | TGRS