Artificial intelligence advancement in addressing cough

Abstract

Cough assessment is a vital component of clinical management for pulmonary disorders, yet effective tools to execute quantification are insufficient. The dominant approach incorporates acoustic evaluation, primarily based on cough sounds. Incorporating artificial intelligence (AI), particularly machine learning and deep learning, is poised to revolutionize this field. Although AI’s application is focused mainly on curative cough management, there is modest yet growing interest in using this technology for preventive medicine. However, these advancements are accompanied by technical, ethical, and legal challenges that must be resolved to leverage their full potential. Furthermore, to ensure reliability and address concerns of medical practitioners about technological replacement, the human-in-loop model should be advocated. This overview summarizes recent advancements in AI for cough medicine, covering its current clinical applications, existing limitations, strategies to address these challenges, and future opportunities.

Keywords: Acoustics; Artificial intelligence; Cough; Cough sound analysis; Deep learning; Machine learning

Core Tip: Evaluating cough for pulmonary conditions relies substantially on acoustic measurement, yet existing quantification tools remain suboptimal. Integrating artificial intelligence (AI), particularly machine learning and deep learning, offers a promising pathway for both therapeutic and preventive applications in cough medicine. Current implementations remain confined to weak AI’s predefined roles, while advances in artificial general intelligence hold the potential to overcome these adaptability constraints. Moreover, harnessing current potential requires resolving key technical, ethical, and legal issues by integrating explainable AI, multimodal hybrid approaches, and robust accountability measures within human-centered frameworks.