Journal of Artificial Intelligence and Digital Health

ABSTRACT

Cardiac and respiratory auscultation remain foundational clinical tools for diagnosing a wide range of cardiopulmonary diseases. However, reliance on clinician expertise and subjective interpretation limits diagnostic accuracy and reproducibility. Recent advances in artificial intelligence (AI), particularly deep learning, have transformed automated acoustic analysis, enabling objective, accurate, and scalable diagnostic support. This review synthesizes current AI methodologies applied to heart and respiratory sound analysis, highlights key clinical applications, addresses challenges including data heterogeneity and model interpretability, and outlines future research directions. We emphasize the transformative potential of AI-powered auscultation to enhance personalized, accessible, and proactive cardiopulmonary care.

Keywords: Artificial Intelligence; Deep Learning; Heart Sounds; Lung Sounds; Auscultation; Cardiac Diagnostics; Respiratory Diagnostics; Explainable AI; Telemedicine; Multi-Organ Acoustic Analysis