Abstract
AI-driven "audiomics" leverages voice and respiratory sounds as noninvasive biomarkers to diagnose and manage pulmonary conditions, including COVID-19, tuberculosis, ILD, asthma, and COPD. By analyzing acoustic features, machine and deep learning enhance diagnostic accuracy and track disease progression. Key applications include cough-based TB detection, smartphone COVID-19 screening, and speech analysis for asthma and COPD monitoring. Ethical challenges like data privacy and standardization remain barriers to clinical adoption. With ongoing research, audiomics holds promise for transforming respiratory diagnostics and personalized care.
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CITATION STYLE
Rajasekar, S. J. S., Saleem, M., Kannan, N., Narayanasamy, K., & Varalakshmi, P. (2025). Artificial Intelligence Powered Audiomics: The Futuristic Biomarker in Pulmonary Medicine - A State-of-the-Art Review. In Studies in Health Technology and Informatics (Vol. 327, pp. 884–885). IOS Press BV. https://doi.org/10.3233/SHTI250491
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