Machines Are Learning Chest Auscultation. Will They Also Become Our Teachers?

• Published in: CHEST Pulmonary
• Main Authors: Hans Pasterkamp, MD, Hasse Melbye, MD, PhD
• Format: Article
• Language: English
• Published: Elsevier 2024-12-01
• Subjects: artificial intelligence; education, medical; heart auscultation; machine learning; respiratory sounds
• Online Access: http://www.sciencedirect.com/science/article/pii/S294978922400045X

Great strides in the development of machine learning techniques are bringing applications of artificial intelligence to ever more areas of clinical medicine. Their potential in the evaluation of visual images and in speech recognition is well established. Recently, the capabilities of machine hearing have been also applied to chest auscultation (ie, the automated analysis, characterization, and classification of heart and lung sounds). Comparing strengths and limitations of human vs machine hearing can help to put these developments in perspective. Humans have multisensory perception (ie, they receive visual and tactile information while auscultating). Humans also surpass machines in the ability to focus attention on listening for specific sounds in noisy environments. Together with information on a patient’s history and presumed medical diagnosis, and with frequent repetition, chest auscultation remains a trainable and valuable human skill. Advantages of machine hearing of chest sounds with digital stethoscopes include not only objective acoustic analysis but also storage of data that allows comparisons over time, presentation in audiovisual format, and wireless communication. Machines can support patient management by relating acoustic analyses to clinical diagnoses, serving as decision support for further investigations, and by monitoring of patients over time. The potential of machines to become teachers of chest auscultation is only now coming into focus. In the near future, assessment of chest sounds will largely remain in the domain of traditional acoustic stethoscopes. However, machines may well be used for training students in different health care professions and nonmedical caregivers, provided that humans remain part of the process.