Artificial Intelligence in Diabetes Management: A New Frontier in Precision Medicine

Purpose: This study explores the integration of global statistical data and machine learning (ML) to advance personalised diabetes management and improve patient outcomes worldwide. Design/methodology/approach: The approach leverages ML algorithms to analyse large-scale, diverse datasets, incorporating real-time inputs from continuous glucose monitoring (CGMs) and wearable devices (WDs). This enables precise, individualised treatment strategies. Findings: The use of global data enhances glycemic control, reduces diabetes-related complications, and empowers both patients and providers through actionable, real-time insights. It promotes adherence and fosters a proactive, patient-centered model of care. Originality/value: This research introduces a novel framework that combines ML and international health data to personalise diabetes care. It highlights a transformative shift from traditional methods to data-driven, individualised interventions, encouraging patient engagement and improving therapeutic outcomes globally.