ARTIFICIAL INTELLIGENCE IN TACKLING CORONAVIRUS AND FUTURE PANDEMICS

Q4 Veterinary
Shagufta Quazi, Sampa Karmakar Singh, R. P. Saha, Arpita Das, M. K. Singh
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引用次数: 0

Abstract

SARS-COV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) was initially tested in Wuhan City, China, in December 2019 and had a devastating impact worldwide, exterminating more than 6 million people as of September 2022. It became the biggest worldwide health crisis since the 1918 influenza outbreak. Viruses generally mutate randomly, so predicting how SARS-CoV-2 will transform over the next few months or years and which forms will predominate is impossible. The possibilities for virus mutation, in theory, are practically endless. Enabling researchers to determine which antibodies have the potential to be most effective against existing and future variations could help machine learning to assist in drug discovery. In the COVID-19 pandemic, AI has benefited four key areas: diagnosis, clinical decision-making for public health, virtual assistance, and therapeutic research. This study conducted a discourse analysis and textual evaluation of AI (deep learning and machine learning) concerning the COVID-19 outbreak. Further, this study also discusses the latest inventions that can be very helpful in future pandemic detection. COVID-19 has already changed our lives, and in the future, we might be able to deal with pandemics like this with the help of AI. This review has also emphasized the legal implications of AI in the battle against COVID-19.
人工智能在应对冠状病毒和未来流行病方面的作用
SARS-COV-2 (严重急性呼吸系统综合征冠状病毒 2 型)于 2019 年 12 月在中国武汉市进行了初步检测,并在全球范围内产生了毁灭性影响,截至 2022 年 9 月,已有 600 多万人因此丧生。它成为自 1918 年流感爆发以来最大的世界性健康危机。病毒通常会随机变异,因此预测 SARS-CoV-2 在未来几个月或几年内将如何转变以及哪种形式将占主导地位是不可能的。从理论上讲,病毒变异的可能性实际上是无穷无尽的。让研究人员能够确定哪些抗体有可能对现有和未来的变异最有效,有助于机器学习协助药物研发。在 COVID-19 大流行中,人工智能已使四个关键领域受益:诊断、公共卫生临床决策、虚拟援助和治疗研究。本研究对有关 COVID-19 爆发的人工智能(深度学习和机器学习)进行了话语分析和文本评估。此外,本研究还讨论了对未来流行病检测非常有帮助的最新发明。COVID-19 已经改变了我们的生活,而在未来,我们或许可以在人工智能的帮助下应对类似的大流行病。这篇评论还强调了人工智能在与 COVID-19 斗争中的法律意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Experimental Biology and Agricultural Sciences
Journal of Experimental Biology and Agricultural Sciences Agricultural and Biological Sciences-Agricultural and Biological Sciences (all)
CiteScore
1.00
自引率
0.00%
发文量
127
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