Trí tuệ nhân tạo trong dự đoán và chẩn đoán các biến chứng của bệnh đái tháo đường

The management of diabetes, a global health challenge, critically depends on effective blood glucose control to prevent and delay severe diabetic complications. Traditionally, this has involved rigorous monitoring and conventional diagnostic methods. However, the rapidly evolving landscape of medical technology, particularly artificial intelligence (AI), is revolutionizing this approach. AI offers unprecedented potential to transform diabetes care by leveraging real-world data from various sources, including smart devices, to create sophisticated models for prediction and diagnosis. This paper provides a comprehensive overview of how AI applications are advancing the early detection and management of diabetes-related complications, thereby enhancing patient quality of life and optimizing clinical outcomes.

Medical professionals, healthcare technology researchers, and academics focused on AI applications in diabetes management and complication prediction.

The primary objective of modern diabetes treatment is to achieve optimal blood glucose control, prevent the progression of diabetic complications, and improve patients' quality of life. Recent advancements in smart devices for diabetes management have significantly increased the availability of vast datasets. This paper explores the transformative role of artificial intelligence (AI), encompassing machine learning and deep learning algorithms, in utilizing this real-world data for the prediction and diagnosis of various diabetes-related complications. Specifically, the article delves into AI's application across several critical areas. It highlights AI's efficacy in predicting gestational diabetes mellitus (GDM), where it has shown improved accuracy over traditional risk factor-based methods. For inpatient hypoglycemia, AI models leveraging large-scale Electronic Health Records (EHR) data help identify patients at risk, allowing for proactive intervention. Furthermore, AI-powered systems are enhancing the early diagnosis and screening of diabetic retinopathy (DR) by analyzing high-resolution fundus images, with some autonomous systems even receiving FDA approval. In the context of diabetic foot ulcers (DFU), AI algorithms assist in lesion detection, localization, and segmentation, crucial for timely treatment. Finally, the paper discusses AI's contribution to predicting diabetic nephropathy (DN), a leading cause of end-stage renal disease, by integrating demographic, physiological, and laboratory data. While AI promises significant improvements in accuracy and personalized management, challenges such as data standardization, ensuring patient data security, and mitigating bias from external data sources remain pertinent. Nevertheless, with increasingly larger and more detailed datasets, AI and related algorithms are poised to be widely adopted for the prediction and diagnosis of diabetes complications globally, enhancing overall patient care.