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.
- A new day is coming in healthcare, where AI will help diagnose and ... ›
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Research suggests that aging affects a brain circuit critical for learning and decision-making.
As people age, they often lose their motivation to learn new things or engage in everyday activities. In a study of mice, MIT neuroscientists have now identified a brain circuit that is critical for maintaining this kind of motivation.
Why not just divide the United States in slices of equal population?
- Slicing up the country in 10 strips of equal population produces two bizarre maps.
- Seattle is the biggest city in the emptiest longitudinal band, San Antonio rules the largest north-south slice.
- Curiously, six cities are the 'capitals' of both their horizontal and vertical deciles.
Sweeping re-alignments<img type="lazy-image" data-runner-src="https://assets.rebelmouse.io/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8yNDYwMTAwOC9vcmlnaW4ucG5nIiwiZXhwaXJlc19hdCI6MTYyMzU3ODA1NH0.u_5xakBvkYwgPtiwLU3z-1e082hBeqwS4Rl1uiJqdF4/img.png?width=980" id="23ff1" class="rm-shortcode" data-rm-shortcode-id="24a5b6ec251a11f3ed7aaefc205dde17" data-rm-shortcode-name="rebelmouse-image" alt="Printed in March 1812, this political cartoon was drawn in reaction to the newly drawn state senate election district of South Essex created by the Massachusetts legislature to favor the Democratic-Republican Party candidates of Governor Elbridge Gerry over the Federalists. The caricature satirizes the bizarre shape of a district in Essex County, Massachusetts, as a dragon-like "monster". Federalist newspaper editors and others at the time likened the district shape to a salamander, and the word gerrymander was a portmanteau of that word and Governor Gerry's last name." />
The original cartoon of the 'Gerry-Mander', published in 1812 in the Boston Centinel.
Image: Elkanah Tisdale (1771-1835), Public Domain.<p>One way for a political party to manipulate the outcome of elections is to 'gerrymander' electoral districts: manipulate their boundaries to increase the likelihood of a favorable outcome (see also #<a href="https://bigthink.com/strange-maps/53-ever-been-ger..." target="_blank">53</a>).</p><p><span></span>The term is almost as old as the United States itself, and the practice continues to disfigure the electoral map to this day. Perhaps these maps can serve as the inspiration for a radical solution. </p><p><span></span>They show the contiguous United States (i.e. without Alaska and Hawaii) sliced latitudinally and longitudinally into ten straight-bordered bands of varying size, so that each contains exactly 10 percent of the population. </p><p><span></span>Although certainly not intended as a reflection on electoral redistricting, it's tempting to see these sweeping re-alignments of the U.S. as a suggestion with some potential in that direction. </p>
United Strips of America<img type="lazy-image" data-runner-src="https://assets.rebelmouse.io/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8yNDYwMTA4MS9vcmlnaW4uanBnIiwiZXhwaXJlc19hdCI6MTY0NzE1MjQ1MX0.WpISo-g15B5O3qXbHXHf-7lQtAainpO7zPuizXWFOGs/img.jpg?width=980" id="d6656" class="rm-shortcode" data-rm-shortcode-id="72ed7c905283f9979ec0f82d451ad261" data-rm-shortcode-name="rebelmouse-image" alt="Reddit user curiouskip used U.S. Census population data to divide the 'Lower 48' into deciles (ten equal parts), each representing about 30.8 million people. Each decile is consigned its most populous city as 'capital'." />
The contiguous United States, divided into horizontal and vertical deciles.
Image: u/curiouskip, reproduced with kind permission.<p>Reddit user curiouskip used U.S. Census population data to divide the 'Lower 48' into deciles (ten equal parts), each representing about 30.8 million people. Each decile is consigned its most populous city as 'capital'.</p><p><span></span>Looking at the top map, which divides the U.S. into 10 longitudinal strips, we see</p><ul><li>Seattle rules the northernmost slice of territory. It is the broadest, and therefore also the emptiest one.</li><li>The Chicago, Omaha, New York City and Indianapolis strips complete the northern half of the country. And indeed: 50 percent of the population occupies roughly one half of the country, from north to south.</li><li>The dividing line between the top and bottom halves of the country runs from just north of the San Francisco Bay to halfway across the Delmarva Peninsula.</li><li>Capital cities of the southern strips are San Jose, Charlotte, Los Angeles, San Diego, and Houston.</li><li>The Houston Strip is divided into two non-contiguous areas. Florida maintains its panhandle, albeit much reduced. </li></ul><p>The bottom map shows the U.S. divided latitudinally into 10 bands of equal population. </p><ul><li>San Jose and Los Angeles both retain their capital status, this time of the two westernmost strips.</li><li>San Antonio is the main city of the Big Empty, more than twice as wide as the second-broadest band.</li><li>The dividing line between America's eastern and western half, population-wise, is far off-center: it skirts the eastern edge of Chicago, making the western half much bigger than the eastern one.</li><li>Houston, Chicago, and Indianapolis also remain the largest cities in their respective bands.</li><li>Further east, Jacksonville and Philadelphia get to rule over their strip of America, while Charlotte and New York City keep winning, both vertically and horizontally.</li></ul><p>Redistricting a country into zones of equal population – and that being your only criterium – will create districts that are randomly diverse, and perhaps also, at least in this case, unmanageably large. </p><p>However, mixing up the political map with a bunch of straight lines as the only instrument is something that has been considered before. Usually, the objective is the wholesale removal of age-old divisions. <br></p>
Perfectly square departments<img type="lazy-image" data-runner-src="https://assets.rebelmouse.io/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8yNDYwMTEzOS9vcmlnaW4uanBnIiwiZXhwaXJlc19hdCI6MTYwOTQyMzIwOH0.kYuf58g0bjsPL9DGPq5PycZ7PDJMnItT0rfrPonOP3k/img.jpg?width=980" id="89a68" class="rm-shortcode" data-rm-shortcode-id="5b81a43e785997bb1f11f72548659a9f" data-rm-shortcode-name="rebelmouse-image" alt="\u200bCh\u00e2ssis figuratif du territoire de la France partag\u00e9 en divisions \u00e9gales entre elles, proposition annex\u00e9e au rapport du 29 septembre 1789 \u00e0 l'Assembl\u00e9e nationale de la commission dite Siey\u00e8s-Thouret" />
France divided into 80-odd geometrical departments: failed proposal by Jacques-Guillaume Thouret (1790).
Image: Centre historique des Archives nationales – Atelier de photographie; public domain.
European Pie<img type="lazy-image" data-runner-src="https://assets.rebelmouse.io/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8yNDYwMTQ0Ny9vcmlnaW4uanBnIiwiZXhwaXJlc19hdCI6MTYxNTE5NDE3OX0.dPcY1tkO7nwkx6IX98Sleh7AmBpDnwlcJLfC_Z-WBlY/img.jpg?width=980" id="b35d7" class="rm-shortcode" data-rm-shortcode-id="84509a9425e13c0dd8fbe00df28a197e" data-rm-shortcode-name="rebelmouse-image" />
In this rather outlandish proposal, continental Europe's 24 cantons center on Vienna.
Image: PJ Mode Collection of Persuasive Maps, Cornell University.<p>And in 1920, an anonymous author – possibly the Austrian P.A. Maas – proposed slicing up Post-World-War-I Europe as a pie, into 24 slices that would center on Vienna's St. Stephen's Cathedral. Each of those slices would be made up of a wide and random variety of linguistic, ethnic, and religious groups – and that would be the point: the better to unite them all into one massive superstate (see also #<a href="https://bigthink.com/strange-maps/a-bizarre-peace-proposal-slice-europe-up-like-a-pie" target="_blank">851</a>).</p><p>Needless to say, both plans never left the drawing board. Would a proposal for the longitudinal and/or latitudinal redistricting of the U.S. have more traction? <br></p>
Coast-to-coast precedents<img type="lazy-image" data-runner-src="https://assets.rebelmouse.io/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8yNDYwMTIwOS9vcmlnaW4uanBnIiwiZXhwaXJlc19hdCI6MTY1MDM2OTE0OX0.52UjcA_YD9Y9UB9_hoSctI_xBrRDALZ2DRLkIo9a8RM/img.jpg?width=980" id="10784" class="rm-shortcode" data-rm-shortcode-id="1999808ea21e11162fdb9181c3912753" data-rm-shortcode-name="rebelmouse-image" alt="Illustration of the Connecticut Charter boundary, 1662" />
Putting the 'connect' into Connecticut: the Nutmeg State extending from the Pacific to the Atlantic.
Image: Connecticuthistory.org<p>Well, for one, coast-to-coast polities have some pedigree in America's past: some of the first colonies had claims that extended from the Atlantic all the way to the Pacific. </p><p>If history had gone entirely the way Connecticut would have wanted, the state would include such inland cities as Detroit, Chicago, and Salt Lake City, and extended to what is now the northern part of California.</p><p>Is such geopolitical weirdness reasonable or feasible today? Absolutely not. But in its randomness, would it be it as unfair as gerrymandering? </p><p><em><br></em></p><p><em>Decile maps of the contiguous United States reproduced with kind permission by u/curiouskip; found <a href="https://www.reddit.com/r/dataisbeautiful/comments/ijyn7p/oc_us_population_deciles_by_latitude_and_longitude/" target="_blank">here</a> on <a href="https://www.reddit.com/" target="_blank">Reddit</a>.<br></em></p><p><strong>Strange Maps #1054</strong></p><p><em>Got a strange map? Let me know at </em><a href="mailto:email@example.com">firstname.lastname@example.org</a><em>.</em></p>
A study finds 1.8 billion trees and shrubs in the Sahara desert.
- AI analysis of satellite images sees trees and shrubs where human eyes can't.
- At the western edge of the Sahara is more significant vegetation than previously suspected.
- Machine learning trained to recognize trees completed the detailed study in hours.
Why this matters<img type="lazy-image" data-runner-src="https://assets.rebelmouse.io/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8yNDU2MDQ1OC9vcmlnaW4uanBnIiwiZXhwaXJlc19hdCI6MTYzOTkyODg5NX0.O3S2DRTyAxh-JZqxGKj9KkC6ndZAloEh4hKhpcyeFDQ/img.jpg?width=980" id="3770d" class="rm-shortcode" data-rm-shortcode-id="3c27b79d4c0600fb6ebb82e650cabec0" data-rm-shortcode-name="rebelmouse-image" />
Area in which trees were located
Credit: University of Copenhagen<p>As important as trees are in fighting climate change, scientists need to know what trees there are, and where, and the study's finding represents a significant addition to the global tree inventory.</p><p>The vegetation Brandt and his colleagues have identified is in the Western Sahara, a region of about 1.3 million square kilometers that includes the desert, <a href="https://en.wikipedia.org/wiki/Sahel" target="_blank">the Sahel</a>, and the <a href="https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/subhumid-zones" target="_blank" rel="noopener noreferrer">sub-humid zones</a> of West Africa.</p><p>These trees and shrubs have been left out of previous tabulations of carbon-processing worldwide forests. Says Brandt, "Trees outside of forested areas are usually not included in climate models, and we know very little about their carbon stocks. They are basically a white spot on maps and an unknown component in the global carbon cycle."</p><p>In addition to being valuable climate-change information, the research can help facilitate strategic development of the region in which the vegetation grows due to a greater understanding of local ecosystems.</p>
Trained for trees<img type="lazy-image" data-runner-src="https://assets.rebelmouse.io/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8yNDU2MDQ3MC9vcmlnaW4uanBnIiwiZXhwaXJlc19hdCI6MTYzNTk5NTI3NH0.fR-n1I2DHBIRPLvXv4g0PVM8ciZwSLWorBUUw2wc-Vk/img.jpg?width=980" id="e02c0" class="rm-shortcode" data-rm-shortcode-id="79955b13661dca8b6e19007935129af1" data-rm-shortcode-name="rebelmouse-image" />
Credit: Martin Brandt/University of Copenhagen<p>There's been an assumption that there's hardly enough vegetation outside of forested areas to be worth counting in areas such as this one. As a result the study represents the first time a significant number of trees — likely in the hundreds of millions when shrubs are subtracted from the overall figure — have been catalogued in the drylands region.</p><p>Members of the university's Department of Computer Science trained a machine-learning module to recognize trees by feeding it thousands of pictures of them. This training left the AI be capable of spotting trees in the tiny details of satellite images supplied by NASA. The task took the AI just hours — it would take a human years to perform an equivalent analysis.</p><p>"This technology has enormous potential when it comes to documenting changes on a global scale and ultimately, in contributing towards global climate goals," says co-author Christian Igel. "It is a motivation for us to develop this type of beneficial artificial intelligence."</p><p>"Indeed," says Brandt says, "I think it marks the beginning of a new scientific era."</p>
Looking ahead and beyond<p>The researchers hope to further refine their AI to provide a more detailed accounting of the trees it identifies in satellite photos.</p><p>The study's senior author, Rasmus Fensholt, says, "we are also interested in using satellites to determine tree species, as tree types are significant in relation to their value to local populations who use wood resources as part of their livelihoods. Trees and their fruit are consumed by both livestock and humans, and when preserved in the fields, trees have a positive effect on crop yields because they improve the balance of water and nutrients."</p><p>Ahead is an expansion of the team's tree hunt to a larger area of Africa, with the long-term goal being the creation of a more comprehensive and accurate global database of trees that grow beyond the boundaries of forests.</p>
Researchers find a key clue to the evolution of bony fish and tetrapods.
- A new study says solar and lunar tide impacts led to the evolution of bony fish and tetrapods.
- The scientists show that tides created tidal pools, stranding fish and forcing them to get out of the water.
- The researchers ran computer simulations to get their results.