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Coronavirus reveals limits of AI health tools

Technology and healthcare companies are racing to roll out new tools to test for and eventually treat the coronavirus epidemic spreading around the world. But one sector that is holding back are the makers of artificial-intelligence-enabled diagnostic tools, increasingly championed by companies, healthcare systems and governments as a substitute for routine doctor-office visits.

In theory, such tools, sometimes called “symptom checkers” or healthcare bots,sound like an obvious short-term fix: they could be used to help assess whether someone has Covid-19, the illness caused by the novel coronavirus, while keeping infected people away from crowded doctor’s offices or emergency rooms where they might spread it.

These tools vary in sophistication. Some use a relatively simple process, like a decision tree, to provide online advice for basic health issues. Other services say they use more advanced technology, like algorithms based on machine learning, that can diagnose problems more precisely.

But some digital-health companies that make such tools say they are wary of updating their algorithms to incorporate questions about the new coronavirus strain. Their hesitancy highlights both how little is known about the spread of Covid-19 and the broader limitations of healthcare technologies marketed as AI in the face of novel, fast-spreading illnesses.

Some companies say they don’t have enough data about the new coronavirus to plug into their existing products. London-based symptom-checking app Your.MD Ltd. recently added a “coronavirus checker” button that leads to a series of questions about symptoms. But it is based on a simple decision tree. The company said it won’t update the more sophisticated technology underpinning its main system, which is based on machine learning.

“We made a decision not to do it through the AI because we haven’t got the underlying science,” said Maureen Baker, Chief Medical Officer for Your.MD. She said it could take 6 to 12 months before sufficient peer-reviewed scientific literature becomes available to help inform the redesign of algorithms used in today’s more advanced symptom checkers.

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Source: The Wall Street Journal, 29 February 2020


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