Collective intelligence out-diagnoses even professionals

The Human Diagnosis Project project is building the world's "open medical intelligence" system.

  • The Human Diagnosis Project can develop medical diagnoses with startling accuracy.
  • The platform combines the knowledge of medical professionals and artifical intelligence.
  • The goal of the project is to provide open, readily available high-level guidance and training to health care professionals across the globe.

The world-class Mayo Clinic is often the place patients go for a second opinion on a medical diagnosis. It's a good thing they do. According to a report issued by the clinic in 2017, 88 percent of them return home with either a completely different diagnosis or a significantly altered one. Only 12 percent receive confirmation of their doctors' original conclusions.

It's hard to overstate the life-and-death importance of medical misdiagnoses, and with all the artificial intelligence and data collection tools out there, you'd think there might be a way to improve on these statistics. This said, the goal of the Human Diagnosis project, or "Human Dx," (a triple pun their site explains) is to create the world's open medical intelligence system, a "collective intelligence" that can produce vastly improved diagnostic accuracy.

In early March, JAMA published the results of an experiment conducted by Human Dx in cooperation with Harvard, and the results were impressive. Where 54 individual human medical specialists correctly diagnosed 156 test cases 66.3 percent of the time, collective intelligence achieved an 85.5 percent accuracy rate. Nine medical professionals contributed to the collective intelligence conclusions.

Human Dx founder Jayanth Komarneni tells Big Think that, "We can get numbers in the 97th, 98th [percentile], and even — if we have sufficiently large numbers of participants — we can get to super intelligent results. That means that it outperforms 100 percent of individual participants."

About Human Dx

The Human Dx project is a partnership between the social, public, and private sectors — in the U.S., it's a 501 (c)(3) not-for-profit/public-benefit corporation. According to Komarneni, Human Dx's business model is as free of cost to users as possible while still generating enough income to be self-sustaining. There are now nearly 20,000 medical professionals in almost 80 countries contributing. Among Human Dx's partners are, as the company states: the American Medical Association, the Association of American Medical Colleges, American Board of Medical Specialties, and the American Board of Internal Medicine. They're also working in collaboration with researchers at Harvard, Johns Hopkins., University of California San Francisco, Berkeley, and MIT.

Open Intelligence

While diagnoses produced by Human Dx do bring together the opinions of multiple medical professionals, it's far from a simple voting system. It incorporates its own massive data set, machine learning, and artificial intelligence in addition to the input from medical professionals to develop its diagnoses. In designing their collective intelligence, says Komarneni, Human Dx had to first re-think the idea of open intelligence itself.

"We believe that open intelligence is the third form of open knowledge," he explains. The first was open source-protocols such as those on which the internet is based, as well as operating systems such as Linux. These protocols enabled the second form, open content: Wikipedia, data libraries, and so on. Open intelligence combines the first two: "And when you think about A.I. in the context of software," says Komarneni, "it really is code which is smartly delivering content to you based on what you put into the system."

The importance of open intelligence is that without it being available at low cost or free, the cost of A.I. is going to be so prohibitive that it'll "exacerbate, as opposed to close, income, health, and other disparities in society," warns Komarneni. Nowhere will the ramification be more serious than in health care, since "there is nothing we care more about than the well-being of the people we love and ourselves."

Image source: koya979/Denis Komolov / Shutterstock / Big Think

How Human Dx collective intelligence works

Collective intelligence in the Human Dx project is not unlike a panel of participants, when are referred to as "agents." Some of these are medical professionals, but they may also include the outputs of other systems. For example, Komarneni mentions that it's entirely possible IBM's Watson could be one of these agents, or even a data set from the National Institutes of Health.

Lingua franca

Of course, individual agents, even the human participants, express themselves in their own ways — is a lump "blue" or "blueberry-colored," for example — not to mention that contributions from some agents such as A.I. or datasets may be in the form of raw data. Before any meaningful synthesis of all these opinions can be performed, the first step is to convert them all into a common language of some sort. Human Dx's AI uses natural language processing, text prediction, and medical ontologies to derive these translations as the process's first step.

Ranking opinions

Human Dx establishes the capability, or CQ ("clinical quotient"), of each agent. To do this they rank agents' skills using test cases with known diagnoses, including "some of the most wickedly complex cases," says Komarneni. This allows Human Dx to determine how accurate agents' diagnoses can be expected to be, and how heavily they should be weighted against other participants' contributions in solving the current case.

A.I. joins the panel

At this point, the agents' inputs are synthesized to derive the most likely diagnosis, and this is combined in an A.I. model with all of the aggregated case data that's ever been captured by Human Dx — interactions in the "tens of millions" — including how "lots of other participants over many other cases have solved these cases." This A.I. model then joins the panel in arriving at the final diagnosis.

"And those [agents] combined," says Komarneni, "are how we can get to results that outperform the vast majority of individual participants."

(TaLaNoVa/Shutterstock/Big Think)

The Harvard and Johns Hopkins studies

The Harvard study published in JAMA is the first public demonstration of the Human Dx system as a diagnostic tool. Working with an international cohort of medical students and professionals, the results were unquestionably amazing. There were 2069 users working 1572 cases — again, these were cases with known correct answers — from the Human Dx data set. About 60 percent of the participants were residents or fellows, 20 percent were attending physicians, and another 20 percent were medical students. In the study, as more medical professionals were added to the collective intelligence "panel," up to nine individuals, its accuracy consistently rose. Physicians who weren't specialists in their test-case areas achieved just a 62.5 percent accuracy score.

A previous study published in JAMA in January, and done in cooperation with Johns Hopkins, looked at Human Dx as an automatic platform for assessing the diagnostic abilities of health care professionals and students. That the scores of participants looking at 11,023 case simulations were consistent with their training level shows, in Komarneni words, "that we provided a valid, quantitative, scalable measure of medical reasoning." While he admits this doesn't sound like a big deal, it is, since it offers a far more accurate and scalable option to current multiple-choice assessments, which have been shown to correspond poorly to real-world diagnostic skills.

The future of health care and Human Dx

Komarneni says that there are basically only two ways to provide global universal health care, a pressing need since, "Almost half the world has no access to essential health services." One way, he says, would be to create a God-like A.I. system to provide health care to everyone, but, "We know that's not going to happen." God-like AI is just too hard, potentially requiring having to know everything about a patient from the tiniest details — say, the quantum behavior of electrons in mitochondria — to the huge, as in the kind of environment a patient lived in as a child.

In addition, Komarneni says, "In a world where data is locked up in many disparate silos, there isn't going to be a single collective agent. There's going to be a collective of many intelligent agents, both human and machine. The key is how do you integrate intelligence into larger buckets of intelligence than can solve the world's hardest problems."

This is where the Human Dx project, and the second approach, comes in. It actually has two components:

  • The first is the expansion of existing medical professionals' diagnostic accuracy skills by providing them access to the Human Dx platform and its collective intelligence as a diagnostic tool.
  • The second is helping to train new professionals, and Human Dx Training is already offering this on the Human Dx site.

For those concerned with privacy in a system such as Human Dx, Komarneni says it'll be a non-issue, explaining with an example. When two people converse, "We don't have access to the underlying data of each others' minds. We're agents that are interacting with each other to gain relevant and useful information from each other." Similarly, Human Dx's system of interacting agents doesn't require the exposure of patients' personal data. What's shared with Human Dx are the conclusions agents draw from that data, not the data itself. In the case of a dataset operating as an agent, the data would be anonymized.

Human Dx's interest in all this is developing a platform it hopes others find uses for. "We believe we're just building the enabling technology that many other stakeholders could use." As examples, Komarneni imagines, "The VA could implement their own version of this. Kaiser Permanente could implement their own version. Employers could contract with us or with their own insurers. You could even also have individual and group practices use Human Dx software to serve patients directly."

Human Dx is currently looking at ways to open up as much of the project for non-professionals as possible, and they've already made a start: On their home page is a diagnosis cloud — mouse over the various blue bubbles to see different conditions, and then click for further details. In addition, just beneath the cloud is a search field with which you can look up diseases and symptoms.

(Human Dx)

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Maps show how CNN lost America to Fox News

Is this proof of a dramatic shift?

Strange Maps
  • Map details dramatic shift from CNN to Fox News over 10-year period
  • Does it show the triumph of "fake news" — or, rather, its defeat?
  • A closer look at the map's legend allows for more complex analyses

Dramatic and misleading

Image: Reddit / SICResearch

The situation today: CNN pushed back to the edges of the country.

Over the course of no more than a decade, America has radically switched favorites when it comes to cable news networks. As this sequence of maps showing TMAs (Television Market Areas) suggests, CNN is out, Fox News is in.

The maps are certainly dramatic, but also a bit misleading. They nevertheless provide some insight into the state of journalism and the public's attitudes toward the press in the US.

Let's zoom in:

  • It's 2008, on the eve of the Obama Era. CNN (blue) dominates the cable news landscape across America. Fox News (red) is an upstart (°1996) with a few regional bastions in the South.
  • By 2010, Fox News has broken out of its southern heartland, colonizing markets in the Midwest and the Northwest — and even northern Maine and southern Alaska.
  • Two years later, Fox News has lost those two outliers, but has filled up in the middle: it now boasts two large, contiguous blocks in the southeast and northwest, almost touching.
  • In 2014, Fox News seems past its prime. The northwestern block has shrunk, the southeastern one has fragmented.
  • Energised by Trump's 2016 presidential campaign, Fox News is back with a vengeance. Not only have Maine and Alaska gone from entirely blue to entirely red, so has most of the rest of the U.S. Fox News has plugged the Nebraska Gap: it's no longer possible to walk from coast to coast across CNN territory.
  • By 2018, the fortunes from a decade earlier have almost reversed. Fox News rules the roost. CNN clings on to the Pacific Coast, New Mexico, Minnesota and parts of the Northeast — plus a smattering of metropolitan areas in the South and Midwest.

"Frightening map"

Image source: Reddit / SICResearch

This sequence of maps, showing America turning from blue to red, elicited strong reactions on the Reddit forum where it was published last week. For some, the takeover by Fox News illustrates the demise of all that's good and fair about news journalism. Among the comments?

  • "The end is near."
  • "The idiocracy grows."
  • "(It's) like a spreading disease."
  • "One of the more frightening maps I've seen."
For others, the maps are less about the rise of Fox News, and more about CNN's self-inflicted downward spiral:
  • "LOL that's what happens when you're fake news!"
  • "CNN went down the toilet on quality."
  • "A Minecraft YouTuber could beat CNN's numbers."
  • "CNN has become more like a high-school production of a news show."

Not a few find fault with both channels, even if not always to the same degree:

  • "That anybody considers either of those networks good news sources is troubling."
  • "Both leave you understanding less rather than more."
  • "This is what happens when you spout bullsh-- for two years straight. People find an alternative — even if it's just different bullsh--."
  • "CNN is sh-- but it's nowhere close to the outright bullsh-- and baseless propaganda Fox News spews."

"Old people learning to Google"

Image: Google Trends

CNN vs. Fox News search terms (200!-2018)

But what do the maps actually show? Created by SICResearch, they do show a huge evolution, but not of both cable news networks' audience size (i.e. Nielsen ratings). The dramatic shift is one in Google search trends. In other words, it shows how often people type in "CNN" or "Fox News" when surfing the web. And that does not necessarily reflect the relative popularity of both networks. As some commenters suggest:

  • "I can't remember the last time that I've searched for a news channel on Google. Is it really that difficult for people to type ''?"
  • "More than anything else, these maps show smart phone proliferation (among older people) more than anything else."
  • "This is a map of how old people and rural areas have learned to use Google in the last decade."
  • "This is basically a map of people who don't understand how the internet works, and it's no surprise that it leans conservative."

A visual image as strong as this map sequence looks designed to elicit a vehement response — and its lack of context offers viewers little new information to challenge their preconceptions. Like the news itself, cartography pretends to be objective, but always has an agenda of its own, even if just by the selection of its topics.

The trick is not to despair of maps (or news) but to get a good sense of the parameters that are in play. And, as is often the case (with both maps and news), what's left out is at least as significant as what's actually shown.

One important point: while Fox News is the sole major purveyor of news and opinion with a conservative/right-wing slant, CNN has more competition in the center/left part of the spectrum, notably from MSNBC.

Another: the average age of cable news viewers — whether they watch CNN or Fox News — is in the mid-60s. As a result of a shift in generational habits, TV viewing is down across the board. Younger people are more comfortable with a "cafeteria" approach to their news menu, selecting alternative and online sources for their information.

It should also be noted, however, that Fox News, according to Harvard's Nieman Lab, dominates Facebook when it comes to engagement among news outlets.

CNN, Fox and MSNBC

Image: Google Trends

CNN vs. Fox (without the 'News'; may include searches for actual foxes). See MSNBC (in yellow) for comparison

For the record, here are the Nielsen ratings for average daily viewer total for the three main cable news networks, for 2018 (compared to 2017):

  • Fox News: 1,425,000 (-5%)
  • MSNBC: 994,000 (+12%)
  • CNN: 706,000 (-9%)

And according to this recent overview, the top 50 of the most popular websites in the U.S. includes in 28th place, and in... 27th place.

The top 5, in descending order, consists of,,, and — the latter being the highest-placed website in the News and Media category.
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