The rise of large language models in wearable health sensing

Abstract

Large language models (LLMs) represent a significant advancement in artificial intelligence (AI), offering versatile tools for natural language generation and understanding. The integration of LLMs into health and medicine is progressing rapidly, coinciding with the rising popularity and accessibility of wearable devices. Modern wearable devices such as smartwatches, heart rate monitors, activity trackers, and smart rings can now continuously monitor and measure various physiological and behavioral data, such as physical activity, motion, heart rate, skin temperature, and sleep. Wearable devices have several applications in biomedical research, health, and medicine. Some of these include self-monitoring, remote patient care, and out-of-clinic assessments. However, the continuous streams of data generated by wearables present significant challenges due to their sheer volume, unique characteristics, and inherent complexity. Although still in its infancy, there is a growing trend toward leveraging pre-trained LLMs to interpret and analyze these wearable-generated data streams for various applications. This opinion piece explores the expanding role of pre-trained LLMs in interpreting, analyzing, and leveraging physiological and behavioral data from wearables and discusses how LLMs offer a promising path forward in this evolving field.