Digital auscultation in clear and present threat of novel respiratory infectious disease: a narrative review - Heeeon Lee; Gun Kim; Jacob Sangwoon Bae (Journal of Yeungnam Medical Science)
The coronavirus disease 2019 pandemic has underscored the limitations of traditional diagnostic methods, particularly in ensuring the safety of healthcare workers and patients during infectious outbreaks. Smartphone-based digital stethoscopes enhanced with artificial intelligence (AI) have emerged as potential tools for addressing these challenges by enabling remote, efficient, and accessible auscultation. Despite advancements, most existing systems depend on additional hardware and external processing, increasing costs and complicating deployment. This review examines the feasibility and limitations of smartphone-based digital stethoscopes powered by AI, focusing on their ability to perform real-time analyses of audible and inaudible sound frequencies. We also explore the regulatory barriers, data storage challenges, and diagnostic accuracy issues that must be addressed to facilitate broader adoption. The implementation of these devices in veterinary medicine is discussed as a practical step toward refining their applications. With targeted improvements and careful consideration of existing limitations, smartphone-based AI stethoscopes could enhance diagnostic capabilities in human and animal healthcare settings.