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• W3115655654 abstract "The clinical severity, rapid transmission and human losses due to coronavirus disease 2019 (Covid-19) have led the World Health Organization to declare it a pandemic. Traditional epidemiological tools are being significantly complemented by recent innovations especially using artificial intelligence (AI) and machine learning. AI-based model systems could improve pattern recognition of disease spread in populations and predictions of outbreaks in different geographical locations. A variable and a minimal amount of data are available for the signs and symptoms of Covid-19, allowing a composite of maximum likelihood algorithms to be employed to enhance the accuracy of disease diagnosis and to identify potential drugs. AI-based forecasting and predictions are expected to complement traditional approaches by helping public health officials to select better response and preparedness measures against Covid-19 cases. AI-based approaches have helped address the key issues but a significant impact on the global healthcare industry is yet to be achieved. The capability of AI to address the challenges may make it a key player in the operation of healthcare systems in future. Here, we present an overview of the prospective applications of the AI model systems in healthcare settings during the ongoing Covid-19 pandemic." @default.
• W3115655654 created "2021-01-05" @default.
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• W3115655654 date "2020-12-19" @default.
• W3115655654 modified "2023-10-12" @default.
• W3115655654 title "How artificial intelligence may help the Covid‐19 pandemic: Pitfalls and lessons for the future" @default.
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• W3115655654 cites W2163087340 @default.
• W3115655654 cites W2180456856 @default.
• W3115655654 cites W2247894828 @default.
• W3115655654 cites W2297600326 @default.
• W3115655654 cites W2339673226 @default.
• W3115655654 cites W2380358550 @default.
• W3115655654 cites W2411662729 @default.
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• W3115655654 cites W2518538438 @default.
• W3115655654 cites W2582336686 @default.
• W3115655654 cites W2607006541 @default.
• W3115655654 cites W2624093302 @default.
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• W3115655654 cites W3006642361 @default.
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• W3115655654 cites W3013758358 @default.
• W3115655654 cites W3014675478 @default.
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• W3115655654 cites W3029848232 @default.
• W3115655654 cites W3031443331 @default.
• W3115655654 cites W3032004606 @default.
• W3115655654 cites W3032017599 @default.
• W3115655654 cites W3032935427 @default.
• W3115655654 cites W3037105702 @default.
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• W3115655654 cites W3037538421 @default.
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• W3115655654 doi "https://doi.org/10.1002/rmv.2205" @default.
• W3115655654 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7883226" @default.
• W3115655654 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/33476063" @default.
• W3115655654 hasPublicationYear "2020" @default.
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