BackgroundDiabetesmellitus represents one of the most urgent global health challenges of the twenty-first century, with its prevalence continuing to rise across both developed and developing regions.Drivenby the combined effects of genetic susceptibility, environmental exposures, and lifestyle factors,diabetesencompasses a complex interplay of insulin resistance, β-cell dysfunction ...
AI-drivensolutions are also emerging to identify noninvasive biomarkers for detectingdiabetesandprediabetes, analyze the macronutrient content of meals using image-based deep learning methods, integrate novel risk prediction tools within electronic health records, and optimize automated insulin delivery systems.
Future horizons indiabetescare must focus on overcoming challenges in data privacy, algorithmic bias, device interoperability, and equity inAI-drivencare while integrating theseinnovationsinto healthcare systems to improve patient outcomes and quality of life.
This systematic review evaluated peer-reviewed studies that examined the integration ofAIandwearabletechnologyindiabetesmanagement, with a focus on clinical and self-managementoutcomes.
Evolution oftechnology-drivendiabetesmanagementparadigms. Comparative framework illustrating three progressive stages in digitaldiabetescare, highlighting transitions in core objectives, technological enablers, data characteristics, interaction modes, and fundamental paradigm shifts from data digitization to artificial intelligence-drivenprecisionmanagement. This paper conducts a ...
Over the last decade,diabetesmanagementtools such as continuous glucose monitors, automated insulin delivery systems, and connected insulin pens have experienced exponential growth. These technologies are more readily being adopted to managediabetesdue to increased availability. This mini-review provides information about recentinnovationsavailable in the United States fordiabetes...
