mlsanityData to 14 Oct 2026

The Role of Artificial Intelligence in Enhancing Insulin Recommendations and Therapy Outcomes

arXiv 2503.18592 ↗First posted 24 Mar 2025cs.AI cs.LG physics.med-phcited by 1

Not on the map. Why?

Abstract (arXiv)

The growing worldwide incidence of diabetes requires more effective approaches for managing blood glucose levels. Insulin delivery systems have advanced significantly, with artificial intelligence (AI) playing a key role in improving their precision and adaptability. AI algorithms, particularly those based on reinforcement learning, allow for personalised insulin dosing by continuously adapting to an individual's responses. Despite these advancements, challenges such as data privacy, algorithm transparency, and accessibility still need to be addressed. Continued progress and validation in AI-driven insulin delivery systems promise to improve therapy outcomes further, offering people more effective and individualised management of their diabetes. This paper presents an overview of current strategies, key challenges, and future directions.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 6 of its 41 references that are in the map. First posted on arXiv 24 Mar 2025.

Builds on

6 of 41 references.

References · 6

  1. 2020Deep Reinforcement Learning for Closed-Loop Blood Glucose Controlcited by 26
  2. 2022Offline Reinforcement Learning for Safer Blood Glucose Control in People with Type 1 Diabetescited by 23
  3. 2020Basal Glucose Control in Type 1 Diabetes using Deep Reinforcement Learning: An In Silico Validationcited by 18
  4. 2019A dual mode adaptive basal-bolus advisor based on reinforcement learningcited by 8
  5. 2019A Dual-Hormone Closed-Loop Delivery System for Type 1 Diabetes Using Deep Reinforcement Learningcited by 4
  6. 2022Estimating a Personalized Basal Insulin Dose from Short-Term Closed-Loop Data in Type 2 Diabetescited by 1

Built on by

Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025Personalised Insulin Adjustment with Reinforcement Learning: An In-Silico Validation for People with Diabetes on Intensive Insulin Treatmentcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Attention Networks for Personalized Mealtime Insulin Dosing in People with Type 1 Diabetes
  2. 2025Precise Insulin Delivery for Artificial Pancreas: A Reinforcement Learning Optimized Adaptive Fuzzy Control Approach
  3. 2026GUIDE: Reinforcement Learning for Behavioral Action Support in Type 1 Diabetes
  4. 2025Flexible Blood Glucose Control: Offline Reinforcement Learning from Human Feedback
  5. 2024DIETS: Diabetic Insulin Management System in Everyday Life ↗
  6. 2025Integrating Neural Differential Forecasting with Safe Reinforcement Learning for Blood Glucose Regulation
  7. 2025Are Large Language Models Dynamic Treatment Planners? An In Silico Study from a Prior Knowledge Injection Angle
  8. 2024An Improved Strategy for Blood Glucose Control Using Multi-Step Deep Reinforcement Learning