Why Spain Will Use AI To Increase Daily Covid-19 Testing

It's faster at processing data, can identify patterns and lessens the risk of exposure by health care professionals - the largest at-risk group-  to those infected. JL

Karl Flinders reports in ComputerWeekly:

To speed up testing of people in Spain, one of the countries hardest hit by the Covid-19 outbreak, with more than 200 deaths so far, Spanish authorities now plan to increase daily testing from 20,000 a day to 80,000, by using four robots to apply artificial intelligence (AI) to testing. Testing has cost and resource limitations. Applying AI and robot technology could help overcome these problems, while reducing medical practitioners’ exposure to the virus.

Technology will be used to speed up testing of people in Spain, one of the countries hardest hit by the Covid-19 outbreak, with more than 200 deaths so far. According to Bloomberg, Spanish authorities now plan to increase daily testing from about 20,000 a day to 80,000, by using four robots to apply artificial intelligence (AI) to testing.

Speaking at a conference on Saturday 21 March, Raquel Yotti, head of Madrid’s health institute, said: “A plan to automate tests through robots has already been designed and Spain has committed to buying four robots that will allow us to execute 80,000 tests per day.”

Because of the ease that coronavirus spreads from person to person, testing has been identified as one of the best ways to control the disease. But testing has cost and resource limitations. Applying AI and robot technology could help overcome these problems, while reducing medical practitioners’ exposure to the virus.

No further details have been given about how the robots will work, but AI is increasingly being designed to work in the healthcare industry by automating some of the work of medical staff, giving them more time to treat patients.

The technology has proved successful in medical trials, including identifying cancer in breast scans.

A research paper from Google Health, published in Nature magazine, has reported that machine learning, based on Google’s TensorFlow algorithm, can be used to reduce false positives in breast cancer scans. A false positive is when a mammogram scan is incorrectly identified as cancerous, and a false negative is when it is wrongly diagnosed as not being cancerous.

In the Google Health paper, based on training an AI algorithm to identify breast cancer using a large representative dataset from the UK and the US, the researchers reported an absolute reduction of 5.7% in false positives in the US dataset, while the UK dataset showed a 1.2% reduction in false positives.