The past eighteen months have been the most testing times for humanity in the 21st century. There's a silver lining, though; the Covid-19 crisis spurred the scientific community into action like never before. The development of vaccines in unprecedented periods has been a testament to the research prowess of the medial community. The AI community is not far behind either – here's a snapshot of five notable research findings with the potential to aid the fight against the deadly virus.

• Precision Medicine for COVID-19: AI technologies can play a key role in preventing, detecting, and monitoring epidemics. This recent paper provides an overview of the latest published literature on the COVID-19 pandemic in four strategic areas: (1) triage, diagnosis, and risk prediction; (2) drug repurposing and development; (3) pharmacogenomics and vaccines; and (4) mining of the medical literature. We highlight how AI-powered health care can enable public health systems to efficiently handle future outbreaks and improve patient outcomes. Read more...
• Future research directions in using AI for the battle against Covid-19: A paper by Deakin University, Australia, presents a survey of AI methods being used in various applications in the fight against the COVID-19 outbreak and outlines the crucial roles of AI research in this unprecedented battle. A summary of COVID-19 related data sources that are available for research purposes is also presented. Research directions on exploring the potentials of AI and enhancing its capabilities and power in the battle are thoroughly discussed. We highlight 13 groups of problems related to the COVID-19 pandemic and point out promising AI methods and tools that can be used to solve those problems. Read more...
• Automatic detection of Covid-19 using chest x-ray images through transfer learning: In this work, researchers from Brazil propose an automatic detection method for COVID-19 infection based on chest X-ray images. The datasets constructed for this study are composed of 194 X-ray images of patients diagnosed with coronavirus and 194 X-ray images of healthy patients. Since few images of patients with COVID-19 are publicly available, we apply the concept of transfer learning for this task. We use different architectures of convolutional neural networks (CNNs) trained on ImageNet, and adapt them to behave as feature extractors for the X-ray images. Then, the CNNs are combined with consolidated machine learning methods, such as k-Nearest Neighbor, Bayes, Random Forest, multilayer perceptron (MLP), and support vector machine (SVM). Read more...
• AI algorithm to detect Covid-19 on Chest Radiographs: DeepCOVID-XR is a deep learning AI algorithm to detect COVID-19 on chest radiographs that was trained and tested on a large clinical data set. DeepCOVID-XR is an ensemble of convolutional neural networks developed to detect COVID-19 on frontal chest radiographs, with reverse-transcription polymerase chain reaction test results as the reference standard. Read more...
• Repositioning of existing drugs for Covid-19: Drug repositioning becomes one of the most feasible approaches for combating COVID-19. This work curates the largest available experimental data set for SARS-CoV-2 or SARS-CoV 3CL (main) protease inhibitors. On the basis of this data set, a research team from Michigan State University has developed validated machine learning models with relatively low root-mean-square error to screen 1553 FDA-approved drugs as well as another 7012 investigational or off-market drugs in DrugBank. Read more...

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