Digital Stethoscope and Artificial Intelligence: Current State, Evidence, Technical Foundations, Clinical Applications, Limitations and Future Directions
Abstract:
The stethoscope, a symbol of clinical medicine for two centuries, is undergoing a technological transformation. Modern digital stethoscopes convert acoustic signals to high-resolution digital waveforms and, when combined with signal processing and machine learning (ML)/artificial intelligence (AI), promise to improve the objectivity, reproducibility and diagnostic yield of auscultation. Accumulating evidence shows AI-assisted auscultation can detect heart murmurs, valvular disease, atrial fibrillation and selected pulmonary pathologies with performance that, in many settings, exceeds unaided clinicians. Large multicenter studies and regulatory clearances have begun to validate commercial algorithms and integrated platforms, and AI-enabled stethoscopes are now being evaluated in primary care and low-resource settings to improve screening and triage. Despite enthusiasm, important challenges remain heterogeneous recording conditions, variable datasets and labels, algorithm generalizability, clinical integration, liability/regulatory frameworks, privacy, and the risk of over-reliance on algorithms. This review summarizes the technological foundations of digital stethoscopes, the state of AI methods applied to heart and lung sounds, evidence from clinical studies and trials, datasets and evaluation practices, regulatory and ethical considerations, and a pragmatic roadmap for deployment and future research priorities.
