Leveraging Large Language Models to Analyze Continuous Glucose Monitoring Data: A Case Study.

ABSTRACT: Continuous glucose monitors (CGM) provide patients and clinicians with valuable insights about glycemic control that aid in diabetes management. The advent of large language models (LLMs), such as GPT-4, has enabled real-time text generation and summarization of medical data. Further, recent advancements have enabled the integration of data analysis features in chatbots, such that raw data can be uploaded and analyzed when prompted. Studying both the accuracy and suitability of LLM-derived data analysis performed on medical time series data, such as CGM data, is an important area of research. The objective of this study was to assess the strengths and limitations of using an LLM to analyze raw CGM data and produce summaries of 14 days of data for patients with type 1 diabetes. This study u

SUBMITTER: Healey E 

PROVIDER: S-EPMC11030468 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature