Episode-Driven Insights: Can Large Language Models Tackle Multimodal Diabetes Data?

This study explores the potential of state-of-the-art large language models (LLM) to scaffold type 1 diabetes management by automating the analysis of multimodal diabetes device data, including blood glucose, carbohydrate, and insulin. By conducting a series of empirically grounded data analysis tasks, such as detecting glycemic episodes, clustering similar episodes into patterns, identifying counterfactual days, and performing visual data analysis, we assess whether models like ChatGPT 4o, Claude 3.5 Sonnet, and Gemini Advanced can offer meaningful insights from diabetes data. Our findings show that ChatGPT 4o demonstrates strong potential in accurately interpreting data in the context of specific glycemic episodes, identifying glycemic patterns, and analyzing patterns. However, limitations in handling edge cases and visual reasoning tasks highlight areas for future development. Using LLMs to automate data analysis tasks and generate narrative summaries could scaffold clinical decision-making in diabetes management, which could make frequent data review feasible for improved patient outcomes.