A Raspberry Pi-based digital stethoscope: Advancing real-time heart and lung sound diagnostics with IoT and machine learning integration

Digital transformation is reshaping auscultation by moving the stethoscope from a purely acoustic instrument to an intelligent, data-driven diagnostic platform. This review critically examines Raspberry Pi 3-based digital stethoscope technologies for heart and lung sound monitoring, with an emphasis on system architecture, sensor integration, signal acquisition, real-time processing, storage, playback, and connectivity. The paper synthesizes current designs and implementation strategies, highlighting how low-cost embedded platforms can improve the repeatability, accessibility, and objectivity of cardiopulmonary assessment compared with conventional acoustic stethoscopes. Particular attention is given to technical performance factors, including filtering, signal-to-noise ratio, latency, audio distortion, noise suppression, and time-frequency analysis, which are essential for detecting clinically significant acoustic features such as heart murmurs, crackles, and wheezes. The review also identifies key limitations affecting clinical translation, including power consumption, device portability, casing size, environmental noise, hardware constraints, and the need for robust validation in real-world settings. Future directions include optimized embedded processing, wireless and IoT-enabled tele-auscultation, cloud-assisted data management, and machine learning models for automated classification and decision support. Overall, Raspberry Pi 3-based digital stethoscopes represent a promising pathway toward affordable, scalable, and clinically meaningful cardiopulmonary monitoring, particularly for point-of-care, remote, and resource-limited healthcare environments. By integrating acoustic sensing, edge computing, and intelligent analytics, these systems can strengthen diagnostic workflows and support the next generation of connected digital healthcare.