Author(s):
Waghamare Suresh, Punam Nikam, Sakshi Ambhore
Email(s):
waghasuresh@gmail.com
DOI:
10.52711/2231-5675.2026.00045 
Address:
Waghamare Suresh*, Punam Nikam*, Sakshi Ambhore
Department of Pharmaceutics, Rashtriya College of Pharmacy, Hatnoor, Maharashtra, India - 431103.
*Corresponding Author
Published In:
Volume - 16,
Issue - 4,
Year - 2026
ABSTRACT:
Diabetes management, particularly insulin dosing for individuals with type 1 and insulin-dependent type 2 diabetes, remains complex due to variability in glucose levels influenced by diet, physical activity, stress, and illness. Conventional insulin regimens often fail to maintain optimal glycemic control, increasing the risk of hypoglycemia and hyperglycemia. Recent advances in Artificial Intelligence (AI), Continuous Glucose Monitoring (CGM), and Automated Insulin Delivery (AID) systems have created new opportunities for personalized insulin therapy. This review highlights the role of AI-driven approaches in optimizing insulin regimens through machine learning, deep learning, reinforcement learning, and model predictive control algorithms. These technologies enable real-time adaptive insulin dosing, predictive glucose monitoring, and automated alerts for abnormal glucose fluctuations. Integration of AI with CGM and closed-loop insulin delivery systems has demonstrated improved Time in Range (TIR), reduced HbA1c levels, and lower hypoglycemia incidence compared to conventional therapy. The review also discusses challenges including algorithm transparency, data security, regulatory considerations, and population-specific adaptability. Emerging innovations such as digital twins, federated learning, and fully autonomous closed-loop systems may further advance precision diabetes management and improve patient outcomes.
Cite this article:
Waghamare Suresh, Punam Nikam, Sakshi Ambhore. Artificial Intelligence-Driven Personalized Insulin Regimens: Integration with Automated Delivery Devices, Continuous Glucose Monitoring and Predictive Algorithms. Asian Journal of Pharmaceutical Analysis. 2026; 16(4):293-1. doi: 10.52711/2231-5675.2026.00045
Cite(Electronic):
Waghamare Suresh, Punam Nikam, Sakshi Ambhore. Artificial Intelligence-Driven Personalized Insulin Regimens: Integration with Automated Delivery Devices, Continuous Glucose Monitoring and Predictive Algorithms. Asian Journal of Pharmaceutical Analysis. 2026; 16(4):293-1. doi: 10.52711/2231-5675.2026.00045 Available on: https://ajpaonline.com/AbstractView.aspx?PID=2026-16-4-7
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