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AI vs. Coronavirus: How Artificial Intelligence is Fighting the Pandemic

The deadly coronavirus, which first appeared in mainland China, has now spread across the world.  The World Health Organization (WHO) has declared it a global pandemic and has reported more than 125,000 cases and 4,500 deaths as of March 12, 2020.

To lend perspective, confirmed cases have already exceeded the number of people infected during the 2002-2003 severe acute respiratory syndrome (SARS) outbreak. While the flu and coronavirus are often compared, coronavirus may be deadlier. On average, the seasonal flu strain kills about 0.1% of infected people. Early death rate estimates in the coronavirus outbreak’s epicenter in Wuhan, China, have been around 2%.

Here’s a look at a number of ways artificial intelligence (AI)-based technologies are being deployed in the fight against the outbreak.

Identifying Outbreaks and Quarantining

AI can predict the number of potential new cases by geography and which types of populations face the greatest risk. It can also help with the enhancement of optimization strategies. For example, machine learning—a subset of AI—has already been employed to research and optimize strategies for quarantine efforts among communities, cities and countries to limit the spread of the virus.

Diagnosis and the Search for A Cure 

AI is also lending a hand in diagnosing coronavirus. Hospitals in China are using AI-based software to scan through CT images of patients’ lungs to look for signs of COVID-19, the infection caused by coronavirus.

However, the medical community has historically cultivated vaccines for comparable viruses, so using AI to look at patterns from similar viruses and detect the attributes that will help build a new vaccine gives scientists a higher probability of success than if they’d started creating one from scratch.

And while it doesn’t guarantee a cure, AI provides an important advantage: It helps scientists more quickly discover relationships among diseases and symptoms, drugs and their effect(s) and the patients who might respond to treatment—insights that, due to the sheer amount of biomedical data, could otherwise be missed in a race against time.

The original article can be found here.

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