If Only My CGM Could Speak: A Privacy-Preserving Agent for Question Answering over Continuous Glucose Data
Continuous glucose monitors (CGMs) used in diabetes care collect rich personal health data that could improve day-to-day self-management. However, current patient platforms only offer static summaries which do not support inquisitive user queries. Large language models (LLMs) could enable free-form inquiries about continuous glucose data, but deploying them over sensitive health records raises privacy and accuracy concerns. In this paper, we present CGM-Agent, a privacy-preserving framework for question answering over personal glucose data. In our design, the LLM serves purely as a reasoning engine that selects analytical functions. All computation occurs locally, and personal health data...
| Authors | Yanjun Cui, Ali Emami, Temiloluwa Prioleau et al. |
|---|---|
| Primary category | cs.AI |
| Published | 18 April 2026 |
| Last updated at source | 18 April 2026 |
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