Artificial Intelligence in Remote Patient Monitoring: A Revolutionary Method for Diabetes-Related Complication Early Identification

Abstract

Objective: Diabetes mellitus poses a major public health challenge in India due to delayed diagnosis, poor glycemic control, and late management of complications. This study aims to review and evaluate the role of artificial intelligence (AI)–driven remote patient monitoring (RPM) systems in improving diabetes management through real-time, predictive, and personalized care.

Methods: A comprehensive review of recent literature was conducted focusing on AI-enabled RPM systems integrating continuous glucose monitors (CGMs), wearable devices, and electronic health records (EHRs). Emphasis was placed on deep learning techniques including long short-term memory (LSTM) networks, autoencoders, transformer models, and privacy-preserving federated learning. Advanced approaches such as hybrid modeling, transfer learning, ensemble methods, and real-time data streaming were analyzed. Case studies relevant to the Indian healthcare context were examined.

Results: AI-driven RPM systems demonstrated significant improvements in glycemic control, early detection of complications, and reduction in emergency hospital visits. Predictive analytics enabled personalized treatment recommendations and proactive interventions. Federated learning approaches ensured data privacy while maintaining model accuracy. Indian case studies indicated enhanced patient engagement, improved clinical outcomes, and cost-effective diabetes care delivery.

Conclusion: AI-powered remote patient monitoring represents a transformative approach to diabetes management in India. By enabling continuous, personalized, and predictive care, these systems can address existing gaps in traditional diabetes treatment. Strategic policy support, ethical governance, and regulatory frameworks are essential to ensure scalable, equitable, and secure implementation across diverse healthcare settings.