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Post-COVID highlights: Challenges and solutions of artificial intelligence techniques for swift identification of COVID-19.
Fang, Yingying; Xing, Xiaodan; Wang, Shiyi; Walsh, Simon; Yang, Guang.
Afiliação
  • Fang Y; National Heart and Lung Institute, Imperial College London, London SW7 2AZ, UK.
  • Xing X; Bioengineering Department, Imperial College London, London W12 7SL, UK.
  • Wang S; National Heart and Lung Institute, Imperial College London, London SW7 2AZ, UK.
  • Walsh S; National Heart and Lung Institute, Imperial College London, London SW7 2AZ, UK.
  • Yang G; National Heart and Lung Institute, Imperial College London, London SW7 2AZ, UK; Bioengineering Department, Imperial College London, London W12 7SL, UK; Imperial-X, Imperial College London, London W12 7SL, UK; Cardiovascular Research Centre, Royal Brompton Hospital, London SW3 6NP, UK; School of Biom
Curr Opin Struct Biol ; 85: 102778, 2024 04.
Article em En | MEDLINE | ID: mdl-38364679
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.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: COVID-19 Limite: Humans Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: COVID-19 Limite: Humans Idioma: En Ano de publicação: 2024 Tipo de documento: Article