Abstract

Objective: From the perspective of clinical nursing, systematically sort out the latest

application achievements of artificial intelligence in smart nursing for diabetes, analyze the core pain points in clinical practice and put forward optimization suggestions, so as to provide references for the clinical implementation of smart nursing for diabetes.

Method: Search for relevant research literature on AI in the field of diabetes care at home and abroad in the past five years. Focus on three dimensions: personalized treatment decision-making, complication risk prediction and diagnosis, and integrated management of "hospital - community - family", sort out the application practice of AI technology, and analyze the existing problems.

Result: AI has achieved remarkable results in diabetes care. Its practical application, such as CGM and double C treatment, has enabled individualized dynamic monitoring and precise intervention of blood glucose. AI models can predict the risk of complications (with an accuracy rate of 82%) and assist in early diagnosis (the sensitivity of DR Screening exceeds 95%). The AI platform breaks down the data barriers across scenarios and improves the timeliness rate of solving home care problems. However, there are still problems such as difficulty in clinical integration, flaws in data models, and the need to improve regulatory ethics. However, there are still problems such as difficulty in clinical integration, flaws in data models, and the need to improve regulatory ethics.

Conclusion: Artificial intelligence can significantly enhance the quality of diabetes care. By leveraging optimization strategies such as "five points and Four Directions" to adapt the care process and improve digital literacy, it can break through the implementation bottlenecks and promote the high-quality development of smart diabetes care.