Perspective on Harnessing Large Language Models to Uncover Insights in Diabetes Wearable Data

Abstract

Abstract Large Language Models (LLMs) have gained significant attention and are increasingly used by researchers. Concurrently, publicly accessible datasets containing individual-level health information are becoming more available. Some of these datasets, such as the recently released Artificial Intelligence Ready and Equitable Atlas for Diabetes Insights (AI-READI) dataset, include individual-level data from digital wearable technologies. The application of LLMs to gain insights about health from wearable sensor data specific to diabetes is underexplored. This study presents a comprehensive evaluation of multiple LLMs, including GPT-3.5, GPT-4, GPT-4o, Gemini, Gemini 1.5 Pro, and Claude 3 Sonnet, on various diabetes research tasks using diverse prompting methods to evaluate their performance and gain new insights into diabetes and glucose dysregulation. Notably, GPT-4o showed promising performance across tasks with a chain-of-thought prompt design (aggregate performance score of 95.5%). Moreover, using this model, we identified new insights from the dataset, such as the heightened sensitivity to stress among diabetic participants during glucose level fluctuations, which underscores the complex interplay between metabolic and psychological factors. These results demonstrate that LLMs can enhance the pace of discovery and also enable automated interpretation of data for users of wearable devices, including both the research team and the individual wearing the device. Meanwhile, we also emphasize the critical limitations, such as privacy and ethical risks and dataset biases, that must be resolved for real-world application in diabetes health settings. This study highlights the potential and challenges of integrating LLMs into diabetes research and, more broadly, wearables, paving the way for future healthcare advancements, particularly in disadvantaged communities.

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