"Hundreds of AI tools have been built to catch covid. None of them helped"

Another piece in the long line of evidence that bum-rushing your way through research is not productive.

“This pandemic was a big test for AI and medicine,” says [Derek] Driggs, who is himself working on a machine-learning tool to help doctors during the pandemic. “It would have gone a long way to getting the public on our side,” he says. “But I don’t think we passed that test.”

The interface between scientists and medicine is quite fraught, but rushing to throw garbage data at black box methods is a recipe for disaster. It undermines confidence.

Science, fundamental research, cannot be rushed. Yes, there are historical examples of development, applied research, under intense time pressure (the Manhattan Project, most famously) but the fundamental groundwork must already be laid. There is no way that an atom bomb could even be speculated about, let alone built, in 3–4 years1.

In the case of COVID, the real (only?) scientific triumph, mRNA vaccines, have been studied for decades; the groundwork was there. All the AI imaging, DIY ventilators, disease models, smartphone apps, all for nothing2.

The positive flip side, however, is that we will likely see some cool science in the future, in five, ten or maybe 15 years. But it will be on a fundamentally unpredictable timeline and, just like mRNA vaccines, its importance won’t be recognized right away.

https://www.technologyreview.com/2021/07/30/1030329/machine-learning-ai-failed-covid-hospital-diagnosis-pandemic/


  1. Leó Szilárd filed a patent concerning nuclear chain reactions in 1934, even introducing the term ‘critical mass’. This was over a decade before the Trinity test. ↩︎

  2. I’m shocked at the number of scientists who pivoted so hard in 2020, so far out of their area of experience, that it’s difficult not to declare them opportunistic sociopaths. ↩︎

Jim Bagrow
Jim Bagrow
Associate Professor of Mathematics & Statistics

My research interests include complex networks, computational social science, and data science.

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