Post-COVID Highlights: Challenges and Solutions of AI Techniques for Swift Identification of COVID-19

Yingying Fang, Xiaodan Xing, Shiyi Wang, Simon Walsh, Guang Yang
9/24/2023
cs.CYcs.AI

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Abstract

Since the onset of the COVID-19 pandemic in 2019, there has been a concerted effort to develop cost-effective, non-invasive, and rapid AI-based tools. These tools were intended to alleviate the burden on healthcare systems, control the rapid spread of the virus, and enhance intervention outcomes, all in response to this unprecedented global crisis. As we transition into a post-COVID era, we retrospectively evaluate these proposed studies and offer a review of the techniques employed in AI diagnostic models, with a focus on the solutions proposed for different challenges. This review endeavors to provide insights into the diverse solutions designed to address the multifaceted challenges that arose during the pandemic. By doing so, we aim to prepare the AI community for the development of AI tools tailored to address public health emergencies effectively.

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Cite this paper

@article{fang2023postcovid,
  title  = {Post-COVID Highlights: Challenges and Solutions of AI Techniques for Swift Identification of COVID-19},
  author = {Yingying Fang and Xiaodan Xing and Shiyi Wang and Simon Walsh and Guang Yang},
  year   = {2023},
  eprint = {2311.06258},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2311.06258v2}
}

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