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Hundreds Of Covid-Catching AI Tools - None Help
In her 1950 short story Contagion, Katherine MacLean described an elaborate system to protect a ship full of space explorers from plagues that might be brought back from alien planets:
Plagues had been known to slay too rapidly and universally to be checked by human treatment. Doctors are not reliable; they die. Therefore spaceways and interplanetary health law demanded that ship equipment for guarding against disease be totally mechanical in operation, rapid and efficient.
(Contagion by Katherine MacLean)
Somewhere near them, in a series of stalls which led around and around like a rabbit maze, Pat was being herded from stall to stall by peremptory mechanical voices, directed to soap and shower, ordered to insert his arm into a slot which took a sample of his blood, given solutions to drink, bathed in germicidal ultraviolet, shaken by sonic blasts, breathing air thick with sprays of germicidal mists, being directed to put his arms into other slots where they were anesthesized and injected with various immunizing solutions.
Interestingly, this elaborate system... didn't quite work. (You can find a link to read it online if you like.)
In response to Covid, hundreds of artificial intelligence tools were developed to diagnose patients or predict the course of their illness. None of them worked, either.
In the end, many hundreds of predictive tools were developed. None of them made a real difference, and some were potentially harmful.
That’s the damning conclusion of multiple studies published in the last few months. In June, the Turing Institute, the UK’s national center for data science and AI, put out a report summing up discussions at a series of workshops it held in late 2020. The clear consensus was that AI tools had made little, if any, impact in the fight against covid...
... large review carried out by Derek Driggs, a machine-learning researcher at the University of Cambridge, and his colleagues, and published in Nature Machine Intelligence. This team zoomed in on deep-learning models for diagnosing covid and predicting patient risk from medical images, such as chest x-rays and chest computer tomography (CT) scans. They looked at 415 published tools and, like Wynants and her colleagues, concluded that none were fit for clinical use.
“This pandemic was a big test for AI and medicine,” says 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.”
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