Limitations of the Cough Sound-Based COVID-19 Diagnosis Artificial Intelligence Model and its Future Direction: Longitudinal Observation Study.
Jina KimYong-Sung ChoiYoung Joo LeeSeung Geun YeoKyoung Won KimMin Seo KimMasoud RahmatiDong-Keon YonJin Seok LeePublished in: Journal of medical Internet research (2024)
While AI models analyzing cough sounds offer a promising noninvasive and rapid screening method for COVID-19, their effectiveness is challenged by the emergence of new virus variants. Ongoing research and adaptations in AI methodologies are crucial to address these limitations. The adaptability of AI models to evolve with the virus underscores their potential as a foundational technology for not only the current pandemic but also future outbreaks, contributing to a more agile and resilient global health infrastructure.
Keyphrases
- artificial intelligence
- coronavirus disease
- sars cov
- global health
- machine learning
- big data
- deep learning
- current status
- public health
- respiratory syndrome coronavirus
- randomized controlled trial
- systematic review
- copy number
- cross sectional
- risk assessment
- gene expression
- disease virus
- loop mediated isothermal amplification