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How Can AI Help in Finding a Coronavirus Cure?

by Mohamed Nabil

Researchers are using AI to accelerate clinical trials to find a Coronavirus cure and are working on genome sequencing using high-performance computing resources.

Many governments, researchers and organisations are feverishly trying to prevent the coronavirus (COVID-19) outbreak from worsening. Increasingly, they’re turning to emerging technologies such as artificial intelligence (AI), blockchain and drones to bolster their efforts, according to CompTIA

More than 90000 coronavirus cases were confirmed in more than 60 countries, resulting in more than 3000 deaths, demonstrating the need to curb the outbreak.

AI technology by itself cannot accurately predict a coronavirus-type event, and AI algorithms that try to do so can only be as good as the data used to build them, cautions Manoj Suvarna, business leader: high-performance computing and AI (North America) at Hewlett Packard Enterprise, and a member of CompTIA’s AI Advisory Council. And therein lies the problem.

Data streams are notoriously inconsistent, unreliable and potentially inaccurate. Several key issues, including a lack of data sharing, a need for local data/models and higher-quality data sets need to be addressed for researchers to better predict the future. Unfortunately, getting better data is easier said than done.

Today, researchers from around the world are using a variety of AI techniques, including natural language processing (to scan mobile phone data, text and messages), facial recognition (to detect signs of fatigue) and infrared temperature sensing (to identify above-normal body temperatures in a crowd), said Suvarna.

“The amount of data getting logged every day, every minute, could assist in building AI algorithms to further narrow down the cause of the spread or the likelihood of the next possible location being affected,” Suvarna said.

“AI tools could also assist in predicting the economic ramifications to businesses and countries based on logistical delays, supply chain disruption, manufacturing productivity, employee attendance and more.”

While sophisticated AI tools have been available for the last several years, there has been limited awareness of real-world applications outside of research laboratories and large enterprises. That should change going forward as the adoption of emerging technologies escalates and use cases become more prevalent, Suvarna said.

“AI practitioners are pushing the limits to see how machines can accelerate the knowledge gathering and analysis phase to shorten the path to insights,” he said.

Researchers are using AI to accelerate clinical trials to find a Coronavirus cure, and there’s a lot of work under way on genome sequencing using high-performance computing resources, Suvarna said. “It will ultimately depend on the types of data being aggregated to see if ML algorithms may be able to detect versus relying solely on human intelligence.”

On the other hand, blockchain technology can be effective in the battle against viral outbreaks if used judiciously, said Neeraj Satija, CEO and CTO of Concordus Applications, and chair of CompTIA’s Blockchain Advisory Council.

“During times like these, there is a desperate and urgent need for collaboration between different organisations, governments and governmental agencies. These entities want systems that can only be accessed and updated by people with the right permission,” Satija said. “The data generated in these systems must be shared in the right format with authorised personnel and should be maintained until perpetuity.”

Blockchain technology can be utilised to maintain data provenance in the fight against epidemics like coronavirus, Satija said That’s important as governments struggle to track the movement of people across cities and countries; pharmaceutical companies try to develop, test and distribute antidotes to find a Coronavirus cure; and healthcare providers seek to be more transparent about the source of these drugs, and keep an immutable record of the administration of medication to affected patients.

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