  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.
