Artificial Intelligence in the Battle against Coronavirus (COVID-19): A Survey and Future Research Directions (2008.07343v4)

Abstract: AI has been applied widely in our daily lives in a variety of ways with numerous success stories. AI has also contributed to dealing with the coronavirus disease (COVID-19) pandemic, which has been happening around the globe. This paper presents a survey of AI methods being used in various applications in the fight against the COVID-19 outbreak and outlines the crucial role of AI research in this unprecedented battle. We touch on areas where AI plays as an essential component, from medical image processing, data analytics, text mining and natural language processing, the Internet of Things, to computational biology and medicine. A summary of COVID-19 related data sources that are available for research purposes is also presented. Research directions on exploring the potential of AI and enhancing its capability and power in the pandemic battle are thoroughly discussed. We identify 13 groups of problems related to the COVID-19 pandemic and highlight promising AI methods and tools that can be used to address these problems. It is envisaged that this study will provide AI researchers and the wider community with an overview of the current status of AI applications, and motivate researchers to harness AI's potential in the fight against COVID-19.

Overview of "Artificial Intelligence in the Battle against Coronavirus (COVID-19): A Survey and Future Research Directions"

The presented paper offers a comprehensive survey of AI applications deployed in the fight against the COVID-19 pandemic. It outlines various AI methodologies and their roles across different domains crucial to combating the outbreak. Spanning from medical image processing to data analytics and NLP, the paper underscores AI’s indispensable contributions to medical and biological solutions during the pandemic.

Key Findings and Contributions

The survey identifies 13 problem groups where AI methods were effective against the COVID-19 pandemic. These groups are poised around prominent AI application domains such as:

1. Medical Image Processing: Deep learning models, including convolutional neural networks (CNNs), have shown efficacy in analyzing chest X-ray and CT images to improve diagnostic accuracy. For instance, COVNet, leveraging a ResNet-50 architecture, demonstrated an AUC of 0.96 for COVID-19 detection using CT images, highlighting deep learning's potential in medical imaging tasks.
2. Data Science for Pandemic Modelling: Data-driven models have expedited the comprehension of virus transmission dynamics. AI-based tools are employed to predict infection rates and evaluate the effects of intervention measures. The paper by Chang et al., adapting the ACEMod model, signifies the utility of AI models for simulating the impacts of various public health strategies.
3. AI and IoT Integration: Incorporating IoT with AI has facilitated applications like real-time monitoring and risk assessment through smartphone sensors. The framework proposed by Maghdid et al. exemplifies how smartphone sensor data can be leveraged for initial COVID-19 detection.
4. NLP for Text Mining: AI-driven text mining has been crucial in extracting meaningful insights from vast amounts of textual data related to COVID-19. Analysis of social media and scholarly articles has helped track public sentiment and identify informative situational data which policymakers can utilize.
5. AI in Computational Biology and Medicine: AI techniques have accelerated drug discovery processes by identifying potential drug compounds. DeepMind’s AlphaFold predictions offered structural insights into SARS-CoV-2 proteins, guiding subsequent experimental validations.

Implications and Future Directions

Practical implications of the survey note AI’s integral role in developing clinical decision-support systems, resource allocation, and enhancing public health responses during pandemics. Theoretically, it challenges the AI research community to improve model interpretability, transparency, and bias reduction, as these factors are critical for clinical adoption. Improved explainable AI frameworks are required to ensure diagnostic suggestions from AI systems are actionable for healthcare professionals.

The potential scalability of AI applications poses a promising future; as more high-quality COVID-19 data becomes available, enhanced models could offer greater predictive accuracy. Furthermore, integration with IoT and edge devices could improve data collection and analysis, optimizing real-time pandemic responses.

The paper suggests future research to address methodological flaws, including biases and reproducibility, in AI-driven studies. It also calls for standardizing COVID-19 related datasets, facilitating easier acceptance and comparison of various AI approaches in real-world scenarios.

Given the pandemic's dynamic nature, the paper argues for a proactive role of AI in preparing for and responding to future outbreaks. This involves advancements in AI-driven surveillance, vaccine development, and efficient dissemination of epidemiological insights to the public health authorities and the general population.

In conclusion, the paper sets a foundational perspective on the existing and potential AI applications to address COVID-19 challenges, catalyzing innovations in health technology and policy-making. Through continued collaborative research and data-centric approaches, AI holds the promise of transforming global pandemic management strategies.