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

arXiv: https://arxiv.org/abs/2503.18592 · HTML: https://mlsanity.com/paper/2503.18592/

## Key facts

- Cited by 1 papers 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 (most cited first, 6 of 6)

- [Deep Reinforcement Learning for Closed-Loop Blood Glucose Control](https://mlsanity.com/paper/2009.09051/) (2020)
- [Offline Reinforcement Learning for Safer Blood Glucose Control in People with Type 1 Diabetes](https://mlsanity.com/paper/2204.03376/) (2022)
- [Basal Glucose Control in Type 1 Diabetes using Deep Reinforcement Learning: An In Silico Validation](https://mlsanity.com/paper/2005.09059/) (2020)
- [A dual mode adaptive basal-bolus advisor based on reinforcement learning](https://mlsanity.com/paper/1901.01816/) (2019)
- [A Dual-Hormone Closed-Loop Delivery System for Type 1 Diabetes Using Deep Reinforcement Learning](https://mlsanity.com/paper/1910.04059/) (2019)
- [Estimating a Personalized Basal Insulin Dose from Short-Term Closed-Loop Data in Type 2 Diabetes](https://mlsanity.com/paper/2211.14149/) (2022)

## Built on by (most cited first, 1 of 1)

- [Personalised Insulin Adjustment with Reinforcement Learning: An In-Silico Validation for People with Diabetes on Intensive Insulin Treatment](https://mlsanity.com/paper/2505.14477/) (2025)

## 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.