AGENTIC-SYSTEMSJuly 17, 20261 min read1 views

Interpretable Language Model for Closed-Loop Type 1 Diabetes Control

Researchers have developed LLM-T1D, a novel approach combining reinforcement learning and large language models to automate insulin delivery for Type 1 Diabetes. The system not only outperforms traditional methods but also explains its decisions in plain language, addressing the 'black-box' challenge in medical AI.

Computer Science > Artificial Intelligence

Title:Interpretable Language Model for Closed-Loop Type 1 Diabetes Control

View PDF HTML (experimental)Abstract:Type 1 Diabetes (T1D) is a chronic, life-threatening autoimmune condition characterized by the complete destruction of insulin-producing pancreatic beta cells. While Artificial Pancreas Systems (APS) powered by Reinforcement Learning (RL) have shown promise in automating insulin delivery, their ``black-box'' nature makes it hard for patients and doctors to trust them fully. This paper presents LLM-T1D, a promising approach that combines the precision of RL with the clear, human-like reasoning of Large Language Models (LLMs) to create a more transparent and reliable insulin pump controller. By training an expert RL system and distilling its knowledge into fine-tuned LLaMA 3.1 8B and Qwen3 8B models, we developed a controller that not only surpasses the RL system's performance but also explains its decisions in plain, understandable language. Tested on the FDA-approved UVA/Padova T1D simulator, the LLM controllers deliver excellent blood sugar control (73.5% Time in Range) while maintaining strict formal safety verification against hallucinations.

Source: arXiv cs.AI Recent

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