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Source: WIRED as of 22-05-2020

Disease diagnosis, drug discovery, robot delivery—artificial intelligence is already powering change in the pandemic’s wake. That’s only the beginning.

ON NEW YEAR’S Eve of last year, the artificial intelligence platform BlueDot picked up an anomaly. It registered a cluster of unusual pneumonia cases in Wuhan, China. BlueDot, based in Toronto, Canada, uses natural language processing and machine learning to track, locate, and report on infectious disease spread. It sends out its alerts to a variety of clients, including health care, government, business, and public health bodies. It had spotted what would come to be known as Covid-19, nine days before the World Health Organization released its statement alerting people to the emergence of a novel coronavirus.

BlueDot’s role in spotting the outbreak was an early example of AI intervention. Artificial intelligence has already played a useful but fragmented role in many aspects of the global fight against the coronavirus. In the past months, AI has been used for prediction, screening, contact alerts, faster diagnosis, automated deliveries, and laboratory drug discovery.

As the pandemic has rolled around the planet, innovative applications of AI have cropped up in many different locations. In South Korea, location-based messaging has been a crucial tool in the battle to reduce the transmission of the disease. Nine out of 10 South Koreans have been getting location-based emergency messages that alert them when they are near a confirmed case.

In China, Alibaba announced an AI algorithm that it says can diagnose suspected cases within 20 seconds (almost 45 times faster than human detection) with 96 percent accuracy. Autonomous vehicles were quickly put to use in scenarios that would have been too dangerous for humans. Robots in China’s Hubei and Guangdong provinces delivered food, medicine, and goods to patients in hospitals or quarantined families, many of whom had lost household breadwinners to the virus. In California, computer scientists are working on systems that can remotely monitor the health of the elderly in their homes and provide alerts if they fall ill with Covid-19 or other conditions.

These snapshots of AI in action against Covid-19 provide a glimpse of what will be possible in the various aspects of health care in the future. We have a long way to go. Truth be told, AI has not had a particularly successful four months in the battle of the pandemic. I would give it a B-minus at best. We have seen how vulnerable our health care systems are: insufficient and imprecise alert responses, inadequately distributed medical supplies, overloaded and fatigued medical staff, not enough hospital beds, and no timely treatments or cures.

Health care systems around the world—even the most advanced ones—are some of the most complicated, hierarchical, and static institutions in society. This time around, AI has been able to help in only pockets of excellence. The reasons for this are simple: Before Covid-19 struck, we did not understand the importance of these areas and act accordingly, and, crucially as far as AI is concerned, we did not have the data to deliver the solutions.

LET’S LOOK TO the future. There are two grounds for optimism.

The first is that data, always the lifeblood of AI, is now flowing. Kaggle, a machine learning and data science platform, is hosting the Covid-19 Open Research Dataset. CORD-19, as it is known, compiles relevant data and adds new research into one centralized hub. The new data set is machine readable, making it easily parsed for AI machine learning purposes. As of publication, there are more than 128,000 scholarly articles on Covid-19, coronavirus, SARS, MERS, and other relevant terms.

The second is that medical scientists and computer scientists across the world are now laser-focused on these problems. Peter Diamandis, founder of the XPrize Foundation, estimated that up to 200 million physicians, scientists, nurses, technologists, and engineers are now taking aim at Covid-19. They are running tens of thousands of experiments and sharing information “with a transparency and at speeds we’ve never seen before.”

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