Artificial Pancreas and Closed-Loop Insulin Delivery: From Early Concepts to AI-Driven Diabetes Automation

Abstract

Closed-loop insulin delivery, or the artificial pancreas, has evolved from an ambitious engineering concept into one of the most consequential advances in diabetes technology. By integrating continuous glucose monitoring, insulin pumps, and control algorithms into a single feedback system, these platforms aim to shift diabetes care from repeated manual correction toward more anticipatory and adaptive glucose regulation. The field has progressed from early proof-of-concept systems to contemporary hybrid closed-loop platforms, reflecting a deeper shift in diabetes management itself: from treating glucose excursions after they occur to trying to blunt them in real time. Its clinical relevance is greatest in type 1 diabetes, where the burden of self-management is high and the consequences of glycemic instability are immediate. This review introduces the major technologies and evidence shaping the field, with emphasis on the control strategies that drive system behavior, and asks which currently available closed-loop systems offer the best balance of glycemic benefit, usability, and translational readiness for routine diabetes care. Across randomized trials and real-world studies, automated insulin delivery has consistently improved time in range, reduced hypoglycemia, and enhanced patient experience, particularly in pediatric populations. At the same time, important limitations remain: sensor lag, the physiologic constraints of subcutaneous insulin, device complexity, cost, and unequal access limit full autonomy and widespread adoption. Looking ahead, the next phase of progress will likely depend on more adaptive artificial intelligence, improved meal detection, multimodal wearable data, and multi-hormone systems that move the field closer to truly physiologic glucose control.