AI-Powered Healthcare Information Systems Securing Diabetes Management Through Integrated Technology Solutions and Enhanced Patient Care Delivery

Abstract

Background: The epidemiology of diabetes in the United States is an acute topic of concern in community health and the economy that has some constraints in infrastructure such as the absence of specialists, ineffective glycemic control. A special opportunity, the introduction of Artificial Intelligence (AI) and Machine Learning (ML) into digital health will allow transferring the process of care delivery to the more proactive and personalized intervention and decrease the constantly increasing healthcare expenditures.

Research Objective: This project will focus mainly on examining the utility of an artificial intelligence system in the delivery of diabetes solutions. The research will also focus on the technology's impact on glycemic outcomes, patient activation, and clinical workflow.

Research Methods: The present review was carried out in accordance with the Preferred Reporting Items of Systematic Review and Meta-Analysis (PRISMA 2020) methodology. The question of the research was formulated on the basis of the PICO framework (Population: Persons with Diabetes (PWDs) in the US; Intervention: AI-Powered Integrated Technology Solutions, including Automated Insulin Delivery (AID), Clinical Decision Support Systems (CDSS), etc.

Conclusion: In spite of this clinical potential, scalable use is limited by fundamental issues, such as the continued absence of standardized device inter-operability and extreme cyber-security risks, especially the susceptibility of ML models to manipulation (e.g. inference-time attacks). Strict regulatory adherence to HIPAA (data privacy) and the Good Machine-Learning Practice (GMLP) of the FDA, which requires Explainable AI (xAI) to provide transparency and accountability to adaptive systems, is required for secure implementation.