Posted on 08/01/2021 7:34:25 PM PDT by algore
When covid-19 struck Europe in March 2020, hospitals were plunged into a health crisis that was still badly understood.
“Doctors really didn’t have a clue how to manage these patients,” says Laure Wynants, an epidemiologist at Maastricht University in the Netherlands, who studies predictive tools.
But there was data coming out of China, which had a four-month head start in the race to beat the pandemic. If machine-learning algorithms could be trained on that data to help doctors understand what they were seeing and make decisions, it just might save lives.
“I thought, ‘If there’s any time that AI could prove its usefulness, it’s now,’” says Wynants. “I had my hopes up.”
Google’s medical AI was super accurate in a lab. Real life was a different story. If AI is really going to make a difference to patients we need to know how it works when real humans get their hands on it, in real situations.
It never happened—but not for lack of effort. Research teams around the world stepped up to help. The AI community, in particular, rushed to develop software that many believed would allow hospitals to diagnose or triage patients faster, bringing much-needed support to the front lines—in theory.
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. Not fit for clinical use
This echoes the results of two major studies that assessed hundreds of predictive tools developed last year. Wynants is lead author of one of them, a review in the British Medical Journal that is still being updated as new tools are released and existing ones tested. She and her colleagues have looked at 232 algorithms for diagnosing patients or predicting how sick those with the disease might get.
They found that none of them were fit for clinical use. Just two have been singled out as being promising enough for future testing.
The simplest move would be for AI teams to collaborate more with clinicians, says Driggs. Researchers also need to share their models and disclose how they were trained so that others can test them and build on them.
A pioneer in this field, Dr. Michael Jordan (no, not him) said that AI today is where every established discipline was 70 years ago. He said chemical engineering started out as chemists talking to manufacturers, materials experts, plant managers, and the like. Through trial and error (and probably some explosions), we started to develop the practice of chemical engineering. Today, you can get a degree in Chem E, but that reflects decades of trial and error and refinement.
The same is true of AI. Today, we have computer programmers, database administrators, statisticians, and maybe economists and physicists that are all trying to make their respective professions turn an ocean of data and optimization routines into a money-making algorithm.
Perhaps like Chem E's early days, the individual subject matter experts in AI all think they're God's Gift to the Planet and have it alllllll figured out, and we don't have to deal with stupid things like learning curves, understanding the subject matter at hand, and pedestrian matters like data cleaning. Heck, they can't even uniformly DEFINE what is 'artificial intelligence.'
Many AI promoters think they have caught lightning in a jar. In reality, their plants are exploding.
When you feed AI garbage stats, it won’t make sense to it, just as it doesn’t to people with brains.
Lies cannot be made consistent.
I believed this works as well as anything: COVID Severity = f(BMI, Systolic BP)
What I haven’t figured out is whether Systolic BP is your uncontrolled number (no meds) or your controlled number (with meds). Nobody has explained that (to my knowledge).
Weird. I saw video after video of people going into the hospitals with cameras to show us how empty they were. I’m assuming all the nurses and doctors were on a different floor making tic tock videos
It is my understanding that AI works with factual data not an illusion. That is the problem.
I understand IBM tried doing a sort of visual neural net review of photos to attempt to catch likely criminals.
They had to give up because it didn’t emote the desired politically correct outcomes.
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