Abstract:
Objective: This study investigates the effectiveness of Artificial Intelligence-driven remote monitoring systems in managing diabetes patients within home-based care settings, with a focus on improving early detection of complications, personalized treatment interventions, patient adherence, and long-term health outcomes in rural communities in Ghana
Methods: The study adopts a mixed-methods approach involving the review and analysis of AI technologies applied in diabetes home care, including machine learning algorithms, predictive analytics, wearable biosensors, telemedicine platforms, and AI-powered virtual health assistants. Patient-generated health data such as blood glucose levels, insulin usage, physical activity, sleep patterns, dietary habits, and cardiovascular indicators are continuously collected and analyzed. Predictive models are used to identify abnormal patterns, forecast potential health risks, and support healthcare professionals in making proactive clinical decisions.
Results: Findings indicate that AI-driven remote monitoring significantly improves glycemic control, reduces emergency hospital admissions, and enhances patient compliance with treatment plans. Real-time alerts generated by AI systems allow for early intervention during episodes of hyperglycemia or hypoglycemia. Personalized recommendations on medication adjustments, dietary planning, and physical activity further improve self-management. Healthcare providers benefit from remote access to patient progress, enabling efficient clinical supervision and reducing the burden on healthcare facilities. The integration of AI also supports precision medicine by tailoring interventions to individual patient profiles.
Conclusion: AI-driven remote monitoring presents a transformative approach to diabetes management by shifting care from hospital-centered treatment to continuous home-based supervision. This study contributes to the growing body of knowledge on intelligent healthcare systems and supports the development of sustainable AI-based diabetes care models for the future.
