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

2025-03-24

AI Summary

The escalating global diabetes burden necessitates safe, interpretable, and equitable AI-driven insulin decision support. This paper introduces the first clinical-grade, personalized insulin recommendation framework integrating privacy preservation (via federated learning) and explainability (via XAI), grounded in a reinforcement learning model that captures dynamic glucose responses and enables real-time dose optimization. Its key innovation lies in the systematic co-design of privacy enhancement and model transparency within the closed-loop insulin decision pipeline—thereby ensuring data security, individualized adaptation, and clinical trustworthiness. Multi-center clinical simulations demonstrate a 12.3% improvement in time-in-range (TIR) and a 37% reduction in hypoglycemic events. The system has received FDA Digital Health Software Pre-Certification, marking a critical step toward deployable, trustworthy AI for diabetes management.

Technology Category

• Machine Learning: Privacy
• Humans and AI: Explainable AI (XAI) for Human Understanding
• Philosophy and Ethics of AI: Privacy & Security

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Security and Privacy: Data transparency and provenance
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

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.

Problem

• AI improves precision in insulin delivery systems
• Personalized insulin dosing using reinforcement learning
• Addressing data privacy and algorithm transparency challenges

Innovation

• AI algorithms personalize insulin dosing
• Reinforcement learning adapts to individual responses
• AI enhances precision in insulin delivery