Journal of Artificial Intelligence and Digital Health

ABSTRACT

The conventional stethoscope, though a clinical mainstay, is limited by subjectivity. Digital stethoscopes integrated with Artificial Intelligence (AI) offer a transformative solution, converting acoustic signals into objective electronic data for automated classification and predictive analytics. This review synthesizes evidence demonstrating that AI-enhanced auscultation significantly boosts diagnostic accuracy and scalability. Studies highlight improved classification of pediatric heart murmurs (AUC up to 0.92), reliable detection of valvular heart disease (e.g., 93.2% sensitivity for aortic stenosis), and identification of lung pathology (wheezes, crackles, pneumonia). Furthermore, these devices proved valuable in screening for left ventricular dysfunction and facilitating triage during the COVID-19 pandemic. Future integration of multimodal sensing (ECG), wearable sensors, and Internet-of-Medical-Things (IoMT) will enable continuous, personalized monitoring. Successful implementation hinges on regulatory oversight and standardization across diverse populations. AI-powered digital stethoscopes are poised to democratize access and advance precision medicine globally.

Keywords: Digital Stethoscope; Artificial Intelligence; Machine Learning; Auscultation; Heart Murmur; Diagnostic Accuracy; Telemedicine; Precision Medicine