Lung auscultation – today and tomorrow- a narrative review

Lung auscultation is a fundamental diagnostic tool for respiratory conditions. Despite advancements in medical technology, the analogue stethoscope remains a crucial tool for diagnosing pulmonary diseases. However, traditional auscultation methods have limitations, including variability in sound interpretation and examiner dependency. This review explores the current standards of lung auscultation, highlighting the challenges and limitations of traditional methods. Further, it examines the potential of digital stethoscopes and artificial intelligence (AI) in enhancing the accuracy and reliability of lung sound analysis. A broad and iterative exploration of the literature was conducted, mainly using PubMed, Scopus, and Google Scholar. The advent of digital stethoscopes and AI presents a potential for more standardized lung auscultation. Although challenging, these technologies can standardize sound categorization, improve diagnostic accuracy, and facilitate remote consultations. There will be a need for large data sets with high quality sound files and outcome measures, and noise during recording needs to be handled. The integration of digital lung auscultation with telemedicine platforms could significantly improve patient monitoring and care, particularly for those with chronic respiratory conditions.

肺部听诊（lung auscultation）是呼吸系统疾病诊断的基础工具。尽管医疗技术不断进步，模拟听诊器（analogue stethoscope）仍是肺部疾病诊断的关键工具。然而，传统听诊方法存在诸多局限，包括声音解读存在个体差异、高度依赖检查者专业水平。本综述梳理了当前肺部听诊的行业标准，着重阐述了传统方法面临的挑战与局限性。此外，本文还探讨了数字听诊器（digital stethoscopes）与人工智能（artificial intelligence）在提升肺音分析准确性与可靠性方面的应用潜力。本次综述通过广泛且迭代的文献调研完成，主要检索数据库包括PubMed、Scopus及Google Scholar。数字听诊器与人工智能的出现，为实现更标准化的肺部听诊提供了可行路径。尽管仍面临诸多挑战，此类技术可实现声音分类标准化、提升诊断准确性，并助力远程问诊。未来仍需构建包含高质量肺音文件与结局指标的大型数据集，同时需妥善处理录制过程中产生的噪声问题。将数字化肺部听诊技术与远程医疗平台相结合，可显著改善患者监护与诊疗服务，尤其针对慢性呼吸系统疾病患者。