Research Gaps, Challenges, and Opportunities in Automated Insulin Delivery Systems
Bibliographic record
Abstract
BACKGROUND: Since the discovery of the life-saving hormone insulin in 1921 by Dr Frederick Banting in 1921, there have been many critical discoveries and technical breakthroughs that have enabled people living with type 1 diabetes (T1D) to live longer, healthier lives. The development of insulin pumps, continuous glucose monitoring (CGM) systems, and automated insulin delivery (AID) systems have enabled people living with T1D to safely manage their glucose, reduce their HbA1c, and improve their overall health and quality of life. Nevertheless, AID systems are not yet designed for all people with T1D, and they perform best during the overnight period when meals and exercise are not occurring. AID systems are not fully automated in that they require the person using the system to announce meals and exercise to the system to avoid dangerous hyper- or hypoglycemia, respectively. METHODS: In this review, which is one of a collection of manuscripts to commemorate the 75th anniversary of the National Institute for Diabetes and Digestive and Kidney Diseases, we celebrate the commercialization of the AID and discuss the major challenges and research gaps that remain to be solved to enable single- and multi-hormone AID systems to more fully support glucose management in people living with T1D. RESULTS: More research is required to design and evaluate more intelligent AID systems that do not require accurate carbohydrate estimations or announcements for meals and exercise. Current AID systems are also not designed to be used by older adults or pregnant people. Results are presented on new AID systems that can automatically respond to meals and exercise. Results are also presented on evaluations of AID systems in older adults and pregnant people. CONCLUSIONS: Next-generation AID systems will need to support all people, including older adults, people during pregnancy, athletes, and people who may be too busy to announce carbohydrates or exercise to the system. Solutions are now becoming available that will enable AID systems to support a broader range of people living with T1D by leveraging the latest technologies in artificial intelligence and adaptive control.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.010 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".