Lots Of AI Tools Promised To Detect COVID-19, But None Were Helpful, Claim Studies

The report highlights that researchers repeated basic errors based on the way they either tested or trained their tools.

Monit KhannaUpdated: Aug 08, 2021, 07:56 IST
4 min read
How A Simple ECG Can Identify COVID Patients At Increased Death Risk

During the COVID-19 pandemic, we saw several nations launch AI applications -- whether for detecting COVID-19 through CT scans or for predicting the number of cases in a particular region. However, a report by MIT Technology Review, none of them really made any difference.

covid-19 AI tools Reuters

Also Read: India's 1st AI Covid-19 Test Facility At IGI Airport Tests Hundreds In Minutes

Laure Wynants who led a study (published in the British Medical Journal) looked at 232 algorithms for diagnosing patients or predicting their sickness. Researchers discovered that not one of the 232 algorithms was actually fit for clinical use. Only two showed some potential that was enough to encourage future testing.

The study by Wynants is based on another larger review that was conducted by Derek Driggs an ML researcher at the University of Cambridge and his team (study published in Nature Machine Intelligence). Here, the team took a microscopic look at 415 deep-learning models for diagnosing the novel coronavirus and predicting patient risk with the help of chest x-rays or CT scans. Even their study concluded that none were capable for clinical use.

What went wrong?

The report highlights that researchers repeated basic errors based on the way they either tested or trained their tools. With incorrect assumptions about the data, it highlighted that the trained models weren't as accurate as they claimed to be.

Most issues were connected with the poor quality of data that the researchers used for creating their tools. Info like medical scans of COVID patients was collected and shared during the pandemic by doctors who were already struggling to treat them. On the other hand, researchers wanted to create solutions and were restricted with the kind of data they were getting their hands on. This resulted in tools being made using data from unknown or mislabeled sources.

Also Read: Two IIT Graduates Build AI Tool To Predict Future Pandemic, Could Win $500K Prize

covid-19 AI tools Reuters

According to Driggs, he refers to these datasets as 'Frankenstein Data Sets' which essentially means data that's spliced together from a variety of sources with high chances of double entries/duplication. This possibly resulted in tools tested on the same data they were trained on, simulating accurate results, which obviously was troublesome.

But this also trashed the origin of certain datasets, causing researchers to miss crucial features that impact the training of the AI models. For instance, some used data sets including chest scans of kids who didn't have covid as instances of what non-covid chest looks like. But with this, the AI could only accurately predict whether the scan is of a child or an adult, instead of looking for COVID-19.

In another one, a group of scans contained mixed scans of patients sleeping on their stomachs and on their back with the former being during severe cases of COVID-19. The AI instead of learning to predict COVID-19 intensity from the way lungs looked did so based on how the patient was sleeping.

Aarogya Setu has similar problems too

While the Indian government was quick to roll out a contact-tracing app for keeping a track of COVID-19 cases in a particular vicinity, the implementation was far from perfect, primarily since most people marking themselves safe even if they'd feel unwell due to the self-marking feature which surely made one question the accuracy of the data.

And even in cases where the data was added accurately, it didn't help the nation fight against the deadly second wave in March that had a catastrophic impact, to say the least.

covid-19 AI tools Reuters

What's the solution?

As per the report, having a quality data set is obviously going to help train the AI a lot better, but in the pandemic world when it's chaotic everywhere, that's a luxury most medical health practitioners didn't have.

Also Read: IBM Watson AI Now Answers Your Questions On COVID-19 In Hindi & English

Researchers ask for more collaboration -- either with clinicians to get better data or with other researchers to create a combined solution that was either better reinforced rather than everyone trying their own project or perfecting an existing solution to make it better.

Wynants said in a statement, ¡°The models are so similar¡ªthey almost all use the same techniques with minor tweaks, the same inputs¡ªand they all make the same mistakes. If all these people making new models instead tested models that were already available, maybe we'd have something that could really help in the clinic by now.¡±

Do you think AI tools will continue to evolve to get better with Covid-19 detection over time? Let us know in the comments section below, and keep visiting Indiatimes.com for all the latest technology news and understanding how technology is impacting our life in a post-Covid world.

28/4/2025 20:49:17
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