AI-Powered Respiratory Telemonitoring: Early Detection of Pulmonary Disorders Using Acoustic and Physiological Signals

Abstract

Respiratory disorders such as asthma, COPD, interstitial lung diseases, and acute respiratory infections account for a significant global disease burden, particularly in low-resource and remote communities where timely diagnosis and continuous clinical supervision remain inadequate. Artificial Intelligence (AI)-powered respiratory telemonitoring has emerged as a transformative approach that enables early detection, risk stratification, and continuous assessment of respiratory health using acoustic and physiological signals. This study investigates the architectures, analytical methodologies, and clinical reliability of AI-driven remote pulmonary monitoring systems. The research assesses the performance of acoustic-based signal analysis (cough detection, breath sound classification, wheeze and crackle recognition) and physiological indicators (oxygen saturation, respiratory rate, heart rate variability, temperature, and airflow measures). Furthermore, the paper highlights validation techniques, model training frameworks, real-world deployment barriers, and ethical considerations related to data privacy, accuracy, and responsible AI use. The findings demonstrate that AI-powered respiratory telemonitoring offers considerable clinical potential by enabling predictive diagnostics, reducing hospital burden, and improving disease management—making it a vital component of the future digital respiratory care ecosystem.