The various bioacoustics signals obtained with auscultation contain complex clinical information that has been traditionally used as biomarkers, however, they are not extensively used in clinical studies owing to their spatiotemporal limitations. In this study, we developed a wearable stethoscope for wireless, skin-attachable, low-power, continuous, real-time auscultation using a lung-sound-monitoring-patch (LSMP). LSMP can monitor respiratory function through a mobile app and classify normal and adventitious breathing by comparing their unique acoustic characteristics. The human heart and breathing sounds from humans can be distinguished from complex sound signals consisting of a mixture of bioacoustic signals and external noise. The performance of the LSMP sensor was further demonstrated in pediatric patients with asthma and elderly chronic obstructive pulmonary disease (COPD) patients where wheezing sounds were classified at specific frequencies. In addition, we developed a novel method for counting wheezing events based on a two-dimensional convolutional neural network deep-learning model constructed de novo and trained with our augmented fundamental lung-sound data set. We implemented a counting algorithm to identify wheezing events in real-time regardless of the respiratory cycle. The artificial intelligence-based adventitious breathing event counter distinguished > 80% of the events (especially wheezing) in long-term clinical applications in patients with COPD.