Innovative applications of artificial intelligence during the COVID-19 pandemic

被引:2
|
作者
Lv, Chenrui [1 ]
Guo, Wenqiang [1 ]
Yin, Xinyi [1 ]
Liu, Liu [2 ,3 ]
Huang, Xinlei [1 ]
Li, Shimin [1 ]
Zhang, Li [1 ]
机构
[1] Huazhong Agr Univ, Wuhan 430070, Peoples R China
[2] Chinese Ctr Dis Control & Prevent, Natl Inst Parasit Dis, Shanghai 200001, Peoples R China
[3] Chinese Ctr Trop Dis Res, Shanghai 200001, Peoples R China
来源
INFECTIOUS MEDICINE | 2024年 / 3卷 / 01期
关键词
COVID-19; Artificial intelligence; Pandemic prediction; Diagnosis; Drug discovery; SYSTEM; RISK; PROGRESSION; PREDICTION; EPIDEMIC; CARE;
D O I
10.1016/j.imj.2024.100095
中图分类号
R51 [传染病];
学科分类号
100401 ;
摘要
The COVID-19 pandemic has created unprecedented challenges worldwide. Artificial intelligence (AI) technologies hold tremendous potential for tackling key aspects of pandemic management and response. In the present review, we discuss the tremendous possibilities of AI technology in addressing the global challenges posed by the COVID-19 pandemic. First, we outline the multiple impacts of the current pandemic on public health, the economy, and society. Next, we focus on the innovative applications of advanced AI technologies in key areas such as COVID-19 prediction, detection, control, and drug discovery for treatment. Specifically, AI-based predictive analytics models can use clinical, epidemiological, and omics data to forecast disease spread and patient outcomes. Additionally, deep neural networks enable rapid diagnosis through medical imaging. Intelligent systems can support risk assessment, decision-making, and social sensing, thereby improving epidemic control and public health policies. Furthermore, high-throughput virtual screening enables AI to accelerate the identification of therapeutic drug candidates and opportunities for drug repurposing. Finally, we discuss future research directions for AI technology in combating COVID-19, emphasizing the importance of interdisciplinary collaboration. Though promising, barriers related to model generalization, data quality, infrastructure readiness, and ethical risks must be addressed to fully translate these innovations into real-world impacts. Multidisciplinary collaboration engaging diverse expertise and stakeholders is imperative for developing robust, responsible, and human-centered AI solutions against COVID-19 and future public health emergencies.
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页数:16
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