Innovative applications of artificial intelligence during the COVID-19 pandemic

Abstract

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.

Keywords: Artificial intelligence; COVID-19; Diagnosis; Drug discovery; Pandemic prediction.

Graphical abstract

Fig. 1

Applications of artificial intelligence (AI) in the COVID-19 pandemic.

Fig. 2

Applications of different artificial intelligence (AI) techniques for COVID-19 prediction. (A) Deep learning AI system using chest computed tomography images and clinical data to predict disease progression in patients with COVID-19 . (B) Combination of deep neural networks and gradient boosting models used to assess the risk of disease progression and deterioration in patients with COVID-19 through analysis of chest X-ray images . (C) Long short-term memory algorithm augmented with an embedded rolling update mechanism for long-term prediction of COVID-19 cases .

Fig. 3

Innovative artificial intelligence approaches for COVID-19 detection. (A) Intelligent COVID-19 diagnostic model, based on the barnacle mating optimization algorithm and cascaded recurrent neural network model, aids in disease diagnosis using low-contrast X-ray images . (B) Deep learning approach using visual transformers developed to create a lateral flow immunoassay platform for smartphones, enabling colorimetric detection of neutralizing antibodies against SARS-CoV-2 in serum .

Fig. 4

Artificial intelligence-empowered control and management of COVID-19. (A) DeepSocial enables automatic monitoring of indoor and outdoor crowds and social distancing to identify areas with the highest likelihood of virus transmission and infection . (B) Combining radio frequency identification devices with fog computing in the Internet of Things (IoT), and capturing social interactions and physical symptoms to classify users .

Fig. 5

Applications of artificial intelligence for COVID-19 drug repurposing. (A) Machine learning model that uses extracellular data encoded with chemical fingerprints to partition molecular fingerprints using a collection of individual tree models to uncover drug molecule characteristics and predict potential candidate drugs . (B) Mechanism-driven neural network approach (DeepCE) that predicts the impact of new chemical entities on differential gene expression profiles by simulating the relationships between chemical substructures and genes, as well as gene–gene interactions .