How A.I. will liberate doctors from keyboards and basements

Giving A.I. a role in health care can help both doctors and patients.

ERIC TOPOL: Technology can't enhance humanity, that it's depersonalizing, that it's going to detract. I actually think it's just the opposite in medicine, because if we can outsource to machines and technology, we can restore the human bond, which has been eroding for decades. So what I mean by deep medicine is really a three part story: The first is what we call deep phenotyping. And that is a very intensive, comprehensive understanding of each person at every level. So that's the idea of knowing all about their biology, not just their genome, their microbiome, and all the things the different layers of the person, but also their physiology through sensors, their anatomy through scans, their environment through sensors as well, and then traditional data.

So that's deep phenotyping. Now, no human being can process all that data, because it's dynamic, and it's actually quite large to deal with. That's why we have deep learning. That's a type of artificial intelligence which takes all of these inputs and it really crystallizes, distills it all. And that gets us to deep empathy. And the deep empathy is when we have this outsourcing to machines and algorithms. We have all of this data, and we now can get back to the human side of this connection. Well, where deep learning works the best today is with images. And so medical images are especially ideal because it turns out that radiologists miss things in more than 30 percent of scans that are done today. So in order to not miss these things, you can train machines to have vision better than humans. The difference here is that the radiologists can put more context in it, but the machines, they're very complementary.

They can pick up things that radiologists wouldn't see, like a nodule on a chest X-ray or an abnormality on an MRA that would be missed because radiologists read 50 to 100 scans per day. There's many times that we just don't see things. So when you bring the two together, you get the best economy. It doesn't mean we're going to eliminate the need for radiologists. It's going to make the accuracy and the speed much better. And what I project is that we're going to see a time when radiologists move out of the basement in the dark and actually connect with patients, because they want to see patients. They want to be able to share their expertise, and they don't have a vested interest about doing an operation or procedure. They just want to report what they find and communicate that. So I think we're going to see a reshaping of radiology because of this remarkable performance enhancement through AI.

There's a lot of use of AI in the hospital setting, because when patients come in, and trying to predict what's going to happen, we're not so good at that generally in medicine. So almost everything you can think of there have been algorithms tested. One example is sepsis. So what's going to happen? Does the person have sepsis, a serious infection? Are they going to decompensate and possibly die from sepsis? We're not so great at that, it turns out, by algorithm. But what we have learned is that we can use the same machine vision, whether it's nurses, doctors, people who are circulating in a room of a hospital, to see whether or not they're doing appropriate handwashing, and to set off a signal that, no, it wasn't done and needs to be done. So there's lots of things about patient safety with machine vision.

So for example, preventing falls, seeing that someone's walking is unsteady. Another great example is in the intensive care unit. Some people can pull out their breathing tube, and now we have machine vision that can monitor that so that we don't have a nurse that has to be in the room all of the time. Well, the biggest thing that we need down is the gift of time. Rather than to have this AI support? And it's at two levels. So if you can get rid of keyboards, or liberate from keyboards, reestablish face-to-face eye contact, that's a good start. It's going to happen. But also the patients now can have algorithms generating their own data, whether it's their heart rhythm, or their skin rash, or a possible urinary tract infection, they can get that diagnosed now by an algorithm. That frees up, again, doctors to take care of more serious matters, and that's what is so exciting if we grab this opportunity, which I don't know if we'll see it again for generations, if ever, because this technology offers that potential. But it won't happen by accident.

If we're not taking on this, really, activism to promote the gift of time and turning inward, as the medical community, if we don't do this, we're going to see even worse squeeze than we have now. This is an opportunity that we just can't miss.

  • Machines can help doctors by spotting abnormalities in X-rays or MRA scans that the physicians themselves may have missed.
  • A.I. can also help physicians by analyzing data and, through the use of algorithms, produce possible diagnoses.
  • The freed up time, as doctors make their rounds, can help physicians establish better connections with their patients, which in turn can lead to better treatment plans.

Astronomers find more than 100,000 "stellar nurseries"

Every star we can see, including our sun, was born in one of these violent clouds.

Credit: NASA / ESA via Getty Images
Surprising Science

This article was originally published on our sister site, Freethink.

An international team of astronomers has conducted the biggest survey of stellar nurseries to date, charting more than 100,000 star-birthing regions across our corner of the universe.

Stellar nurseries: Outer space is filled with clouds of dust and gas called nebulae. In some of these nebulae, gravity will pull the dust and gas into clumps that eventually get so big, they collapse on themselves — and a star is born.

These star-birthing nebulae are known as stellar nurseries.

The challenge: Stars are a key part of the universe — they lead to the formation of planets and produce the elements needed to create life as we know it. A better understanding of stars, then, means a better understanding of the universe — but there's still a lot we don't know about star formation.

This is partly because it's hard to see what's going on in stellar nurseries — the clouds of dust obscure optical telescopes' view — and also because there are just so many of them that it's hard to know what the average nursery is like.

The survey: The astronomers conducted their survey of stellar nurseries using the massive ALMA telescope array in Chile. Because ALMA is a radio telescope, it captures the radio waves emanating from celestial objects, rather than the light.

"The new thing ... is that we can use ALMA to take pictures of many galaxies, and these pictures are as sharp and detailed as those taken by optical telescopes," Jiayi Sun, an Ohio State University (OSU) researcher, said in a press release.

"This just hasn't been possible before."

Over the course of the five-year survey, the group was able to chart more than 100,000 stellar nurseries across more than 90 nearby galaxies, expanding the amount of available data on the celestial objects tenfold, according to OSU researcher Adam Leroy.

New insights: The survey is already yielding new insights into stellar nurseries, including the fact that they appear to be more diverse than previously thought.

"For a long time, conventional wisdom among astronomers was that all stellar nurseries looked more or less the same," Sun said. "But with this survey we can see that this is really not the case."

"While there are some similarities, the nature and appearance of these nurseries change within and among galaxies," he continued, "just like cities or trees may vary in important ways as you go from place to place across the world."

Astronomers have also learned from the survey that stellar nurseries aren't particularly efficient at producing stars and tend to live for only 10 to 30 million years, which isn't very long on a universal scale.

Looking ahead: Data from the survey is now publicly available, so expect to see other researchers using it to make their own observations about stellar nurseries in the future.

"We have an incredible dataset here that will continue to be useful," Leroy said. "This is really a new view of galaxies and we expect to be learning from it for years to come."

Protecting space stations from deadly space debris

Tiny specks of space debris can move faster than bullets and cause way more damage. Cleaning it up is imperative.

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  • NASA estimates that more than 500,000 pieces of space trash larger than a marble are currently in orbit. Estimates exceed 128 million pieces when factoring in smaller pieces from collisions. At 17,500 MPH, even a paint chip can cause serious damage.
  • To prevent this untrackable space debris from taking out satellites and putting astronauts in danger, scientists have been working on ways to retrieve large objects before they collide and create more problems.
  • The team at Clearspace, in collaboration with the European Space Agency, is on a mission to capture one such object using an autonomous spacecraft with claw-like arms. It's an expensive and very tricky mission, but one that could have a major impact on the future of space exploration.

This is the first episode of Just Might Work, an original series by Freethink, focused on surprising solutions to our biggest problems.

Catch more Just Might Work episodes on their channel:
https://www.freethink.com/shows/just-might-work

Credit: fergregory via Adobe Stock
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Meet the worm with a jaw of metal

Metal-like materials have been discovered in a very strange place.

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