Advances in battery technology developed embedded processors with low power consumption and integrated sensors to make stethoscopes wearable and wireless, so that doctors can examine patients from a distance. However, it is difficult for doctors to examine these patients and auscultation is hardly done. Moreover, the demand of patients in hard-to-reach area for telemedicine is increasing nowadays. Many patients with chronic diseases or limited mobility stay in nursing facilities or at home often without a medical practitioner. When doctors examine patients with a stethoscope, auscultation must be implemented by contacting the stethoscope on the body of patients. Īnother drawback of auscultation is the impossibility of remote care. Besides, there have been published studies on artificial intelligence (AI)-assisted auscultation which recognizes the pattern of sounds and identifies their abnormalities, and some digital stethoscopes already adopted machine learning (ML) algorithms. Recent technical advances have allowed the recording of lung sounds with a digital stethoscope by electronical intensification of the sounds, and the sharing of recorded sound via blue-tooth transmission. To improve this problem, there have been efforts to implement a standardized system to record and share lung sounds to analyze them accurately. This discrepancy can potentially lead to inaccurate diagnosis and mistreatment. The ability to recognize and differentiate the abnormal sounds depends on the listener's experience and knowledge. This phenomenon may be caused by the inherent subjectivity.

However, as chest images are developed, the degree of dependence on auscultation is relatively decreasing. So far, the stethoscope has been widely used and adopted as the physician’s primary medical tool. Since then, the stethoscope has gradually changed to a device with a binaural form, flexible tubing, and a rigid diaphragm. In 1817, French doctor Rene Laennec invented an auscultation tool and it enabled him to listen to internal noises of patients. It is particularly useful in respiratory diseases, and abnormal respiratory sounds provide information on various pathological conditions of lungs and bronchi. A stethoscope is considered one of the most valuable medical devices because it is non-invasive, available in real-time, and much informative. In the long-standing history of mankind, auscultation has long been widely used for the examination of patients. There are still challenges to overcome, such as the analysis of complex and mixed respiratory sounds and noise filtering, but continuous research and technological development will facilitate the transition to a new era of a wearable and smart stethoscope. In addition, the current advances in battery technology, embedded processors with low power consumption, and integrated sensors make possible the development of wearable and wireless stethoscopes, which can help to examine patients living in areas of a shortage of doctors or those who need isolation. Deep learning-based analysis with an automatic feature extractor and convoluted neural network classifier has been applied for the accurate analysis of respiratory sounds. In particular, the recordable stethoscope made it possible to analyze breathing sounds using artificial intelligence, especially based on neural network. Recent innovative digital stethoscopes have overcome the limitations and enabled clinicians to store and share the sounds for education and discussion. Conventional stethoscope could not record the respiratory sounds, so it was impossible to share the sounds. Although auscultation is non-invasive, rapid, and inexpensive, it has intrinsic limitations such as inter-listener variability and subjectivity, and the examination must be performed face-to-face.

Auscultation with stethoscope has been an essential tool for diagnosing the patients with respiratory disease.

Lung sounds and locations