top of page
  • Writer's pictureTriple Helix

Automated Insulin Delivery Systems and the Future of Diabetes Management

Written by Jamie Saito ‘25

Edited by Surya Khatri ‘24

Automated insulin delivery (AID) systems are becoming increasingly popular for patients with Type 1 Diabetes. These systems not only monitor blood glucose levels but also attempt to incorporate algorithms to predict how insulin should be delivered to the user. Though many of these devices are not yet FDA approved, they have managed to grow in popularity due to public collaboration. Users of AIDs have made their algorithms available to the public via open source software. Despite the limited amount of clinical data about this new topic, recent studies show promising clinical results that these devices may be the future of diabetes treatment.

Under normal physiological conditions, beta cells produce and secrete a hormone called insulin, which helps to modulate blood glucose levels throughout the body [2]. Type 1 Diabetes is an autoimmune disease that attacks these beta cells in the pancreas, preventing the body from effectively breaking down sugars and depriving it of necessary fuel [3]. To compensate for this lack of energy, the liver will begin breaking down fat instead of glucose. However, this process releases ketones as a byproduct, which can accumulate in the body and cause a life-threatening complication called diabetes ketoacidosis [4]. Symptoms of Type 1 Diabetes usually begin to develop in children or young adults and require continual management after diagnosis, such as monitoring blood sugar levels. Additionally, patients must take some form of insulin to compensate for the destruction of beta cells and ensure that sugar can be broken down throughout the body.

Insulin injections are often used to regulate blood glucose levels in patients with Type 1 Diabetes. However, this requires multiple daily injections at scheduled times throughout the day, which may not be convenient or ideal for many people [5]. Instead, patients may opt to use a pump for delivery of insulin, allowing them to regulate their blood glucose levels throughout the day. Conventionally, these devices release a “once-daily basal insulin injection and three (or more, if snacks are eaten) combined mealtime and correction injections per day” [2]. Traditionally, this system—classified as an open loop system—would have a set amount of insulin be delivered throughout the day independent of blood sugar levels.

New technology has been able to couple these pumps with glucose monitoring. These systems are classified as hybrid-closed loop systems, meaning they incorporate some automation into the classic pump approach. Currently, two of these devices have been FDA approved and introduced into the clinical world. Both allow the user to prompt an insulin delivery during mealtime to prevent a sharp increase in glucose levels. They are programmed with one of two approaches: treat-to-range or treat-to-target. In a treat-to-range approach, “an algorithm will adjust preprogrammed basal rates only if predicted to be outside a specific range” [2]. This can ensure that insulin levels remain relatively constant throughout the body and prevent drastic increases or drops in blood glucose levels. A treat-to-target approach “determines higher or lower insulin delivery depending on whether current sensor glucose is above or below a specific target” [2]. While these methods are both effective at preventing life-threatening changes like diabetic ketoacidosis, they still require that the user manually prompts an insulin dose at mealtime. Because of this, scientists are still hoping that this process can be further automated.

Figure 1: Current and future glucose management options for patients with Type 1 Diabetes.

Current research has increased its focus on synthesizing new technology with these conventional prompts to create more intuitive insulin delivery systems. Using algorithmic models, scientists hope to develop a closed loop system that can automatically deliver insulin based on blood glucose levels and other physiological factors [6]. These systems, called automated insulin delivery (AID) systems, not only monitor current blood glucose levels but also use algorithms to predict what changes will occur in the future. This can allow insulin delivery to be more targeted and precise. Currently, insulin is delivered at a set amount, usually determined by physicians. However, switching to an automated approach may allow more timely adjustments in insulin delivery approaches, similar to how the body functions.

Unlike many other technologies, these AID systems are primarily being created and tested by Type 1 Diabetes patients. This “patient-led movement has developed ways for people with diabetes to connect (or hack) existing commercially available pumps and sensors to open-access algorithms on their smartphones to create their own hybrid closed-loop systems,” which can then be shared through open source code [2]. However, because of this patient-driven development, there is a lack of concrete clinical data for these methods [1]. Before being commercially approved, researchers want to understand whether these devices are effective and what disadvantages may come with them.

Recent studies are beginning to illustrate the effectiveness of AID systems. In September 2022, Burnside et. all illustrated that AID systems maintained blood glucose levels more effectively than conventional systems. Over the course of 24 weeks, researchers tracked the blood glucose levels of patients using an AID system and patients using a sensor-augmented insulin pump. Comparing the results from both groups, this study illustrates that “patients who were using the open-source AID system had 3 hours 21 minutes more time in the target glucose range per day than those who were using sensor-augmented insulin-pump therapy” [7]. Another study published in September 2021 found similar results over the course of 20 days. In their trials, the closed loop insulin delivery system “was associated with over 3.5 additional hours every day spent in the target glucose range” [8]. These promising clinical results are an important step in confirming the effectiveness of AID systems. However, further research should be conducted to confirm that these positive outcomes can generalize across the population before AIDs are approved in the medical community.

The development of fully automated insulin delivery systems holds the potential to change Type 1 Diabetes treatment. Though further clinical research is needed before these devices can become commercially and clinically approved, these systems seem to be effective in maintaining safe blood glucose levels. Ultimately, there is potential to create devices that can act like an artificial pancreas, modulating insulin levels so that blood sugar will stay within the normal range without user input. This would remove the burden of manual data collection for Type 1 Diabetes patients and change standard treatment options.



1. Larkin HD. Open-source Closed-Loop System Is Effective for Type 1 Diabetes. JAMA. 2022 Oct 11;328(14):1387–8.

2. Perkins BA, Sherr JL, Mathieu C. Type 1 diabetes glycemic management: Insulin therapy, glucose monitoring, and automation. Science. 2021 Jul 30;373(6554):522–7.

3. CDC. What Is Type 1 Diabetes? [Internet]. Centers for Disease Control and Prevention. 2022 [cited 2022 Nov 4]. Available from:

4. CDC. Diabetic Ketoacidosis [Internet]. Centers for Disease Control and Prevention. 2021 [cited 2022 Nov 5]. Available from:

5. Administering Insulin [Internet]. JDRF. [cited 2022 Nov 4]. Available from:

6. Ankrum J. Outsourcing glucose management to AI. Sci Transl Med. 2020 Oct 7;12(564):eabe8120.

7. Burnside MJ, Lewis DM, Crocket HR, Meier RA, Williman JA, Sanders OJ, et al. Open-Source Automated Insulin Delivery in Type 1 Diabetes. N Engl J Med. 2022 Sep 8;387(10):869–81.

8. Boughton CK, Tripyla A, Hartnell S, Daly A, Herzig D, Wilinska ME, et al. Fully automated closed-loop glucose control compared with standard insulin therapy in adults with type 2 diabetes requiring dialysis: an open-label, randomized crossover trial. Nat Med. 2021 Aug;27(8):1471–6.

9. Nimri R, Battelino T, Laffel LM, Slover RH, Schatz D, Weinzimer SA, et al. Insulin dose optimization using an automated artificial intelligence-based decision support system in youths with type 1 diabetes. Nat Med. 2020 Sep;26(9):1380–4.

[Figure Citation] Type 1 diabetes glycemic management: Insulin therapy, glucose monitoring, and automation. [Internet] [cited Nov 4 2022] Available from:

9 views0 comments


bottom of page