Developing personalized diabetes management plans using artificial intelligence and machine learning

摘要

Diabetes is a lifelong condition which is associated with abnormally high blood sugar levels due to insufficient production of insulin or failure of the body to use the hormone efficiently. Due to a rising number of diabetes cases globally, the need to develop efficient and targeted approaches for the disease is more pressing than pre/application. Artificial intelligence (AI) and machine learning (ML) are among the most significant innovations in the healthcare industry, creating new positive directions for creating individualized plans for diabetes mellitus management. AI and ML are not just limited to handling medical and patients’ data, but it helps in robot assisted surgeries, virtual nursing professionals, and computer aided diagnosis. These technologies are not deploying human practitioners out of service, but instead heightening their capabilities culminating in a health win and decrease in cost. PROAC supports further research and development of these fast-growing technologies in healthcare, as we find ourselves on the brink of revolution that might change disease management and customization of treatment for many more years to come. This review has therefore involved using different electronic databases, including PubMed, Scopus and Google Scholar, to search for relevant published literature. The application of AI and ML has proved promising, and the research concluded that it can help develop a risk assessment model for early diagnosis and prevention of diabetes. Such algorithms look at a combination of multiple risk factors including family history, genetics, nutrition, biochemical markers, and physical activity level, which puts those with higher risk on special diet, exercise regimens or screening. Further, AI endorsing, decision support systems may provide tailored recommendations regarding treatment schedules based on patients’ characteristics such as insulin response, diet and exercise. Additionally, the continuous glucose monitoring (CGM) devices integrable with the machine learning algorithms can inform real-time information about glucose behaviors to make relevant alterations to doses of insulin and a dietary intake. This paper presents various benefits of adopting AI and ML in diabetes care: heterogeneous and accurate risk prediction, individualized therapies, and observation. But the problem of data quality, data privacy issues and the issue of interpretability and explainability of the trained AI models remains a problem in the use of AI in automated scheduling. The approaches require interdisciplinary collaborations across the medical field, data analytics, and policy departments in order to be properly applied and used.