Diabetes mellitus is one of the largest global public health concerns, imposing a heavy global burden on public health as well as socio-economic development. The International Diabetes Federation (IDF) estimated that 463 million adults lived with diabetes worldwide in 2019, with a projected increase to 700 million by 2045 if no effective prevention methods are adopted. Diabetes mellitus is also a leading cause of mortality and the second leading cause of reduced life expectancy. Despite the efforts of medical staff, Diabetic Associations, the Endocrinology Society, and government health management agencies, there are still many challenges in the management of diabetes, including unequal distribution of medical resources on a global scale and within a country or region, insufficient educational resources for diabetes, high patient numbers and limited medical staff, and low medical compliance. These barriers to efficient diabetes management lead to a reduction in patients with good blood glucose control and high rates of diabetes-related complications, driving healthcare costs up and reducing the quality of patients’ lives.

Recently, with tremendous advancements in healthcare delivery technologies such as websites, smartphone applications, telemedicine, mobile health, machine-learning technology, and artificial intelligence, there are some improvements in case finding, blood glucose control, quality of life, and diabetic complication detection. For example, some papers have demonstrated that blood glucose control and quality of life have been improved as a result of telemedicine, apps, etc. The U.S. Food and Drug Administration has permitted marketing of the first medical device to use artificial intelligence to screen for diabetic retinopathy in diabetic populations. There is a significant opportunity to improve the efficiency of diabetic education and compliance of patients, achieve better metabolic control, increase efficiency in diabetes management, and increase rates of patient involvement in diabetes self-management.

This Special Issue aims to collate original research articles and review articles aimed at exploring the risk, genetic and pathogenesis findings of diabetes and its complications based on internet technology, machine-learning technology, and artificial intelligence. This includes diabetic education, diabetes and, especially, hypoglycemia prevention, blood glucose control, metabolic control, and quality of life based on internet technology, smartphone (apps, WeChat, etc), machine-learning technology, and artificial intelligence.

Potential topics include but are not limited to the following:

• Genetic and pathogenesis findings on diabetes based on information technology
• Risk finding of artificial intelligence and/or machine-learning technology in diabetes and its complications
• Diabetes education based on websites, apps, WeChat, telemedicine, etc.
• Weight management, control of blood glucose, blood pressure, lipid profile of diabetes based on websites, apps, WeChat, telemedicine, etc.
• Medical compliance, hypoglycemia, and life quality of diabetes based on websites, apps, WeChat, telemedicine, etc.
• The diagnosis, screening, and treatment of diabetes and its complications based on artificial intelligence
• Machine-learning technology in diabetes and its complications
• Clinical big data and diabetes mellitus
• Cost-effectiveness and efficiency of diabetes management with advanced information technologies