ALPHA: AnomaLous Physiological Health Assessment Using Large Language Models

TL;DR

This paper demonstrates that Large Language Models can accurately analyze physiological data from FDA-approved devices, achieving high precision in health monitoring and interpretation tasks, thus advancing AI-driven personal health assessment.

Contribution

The study introduces a novel application of LLMs in interpreting physiological data for health monitoring, showing their effectiveness in real-world simulated environments.

Findings

LLMs achieved less than 1 bpm MAE in heart rate estimation.

LLMs achieved less than 1% MAE in oxygen saturation (SpO2) evaluation.

Overall health assessment accuracy exceeded 85%.

Abstract

This study concentrates on evaluating the efficacy of Large Language Models (LLMs) in healthcare, with a specific focus on their application in personal anomalous health monitoring. Our research primarily investigates the capabilities of LLMs in interpreting and analyzing physiological data obtained from FDA-approved devices. We conducted an extensive analysis using anomalous physiological data gathered in a simulated low-air-pressure plateau environment. This allowed us to assess the precision and reliability of LLMs in understanding and evaluating users' health status with notable specificity. Our findings reveal that LLMs exhibit exceptional performance in determining medical indicators, including a Mean Absolute Error (MAE) of less than 1 beat per minute for heart rate and less than 1% for oxygen saturation (SpO2). Furthermore, the Mean Absolute Percentage Error (MAPE) for these evaluations remained below 1%, with the overall accuracy of health assessments surpassing 85%. In image analysis tasks, such as interpreting photoplethysmography (PPG) data, our specially adapted GPT models demonstrated remarkable proficiency, achieving less than 1 bpm error in cycle count and 7.28 MAE for heart rate estimation. This study highlights LLMs' dual role as health data analysis tools and pivotal elements in advanced AI health assistants, offering personalized health insights and recommendations within the future health assistant framework.