A Data Management Case to Embrace Artificial Intelligence

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artificial intelligenceOne of the subjects of debate surrounding Artificial Intelligence (AI) is whether, or how much, its progress threatens taking humans out of the equation in industries of every type. At the Aspen Ideas Festival this summer, Coursera co-founder Andrew Ng was one of a panel commenting on the trend, noting that Artificial Intelligence is on the path to transform every major sector, from healthcare to education to manufacturing, and that it will displace a lot of jobs while doing it, for both blue- and white-collar workers.

Not everyone sees the technology as taking humans out of the equation though. For Walter De Brouwer, who has a history in internet entrepreneurship and is founder and CEO of the recently-launched computational linguistics company doc.ai, humans haven’t yet gone far enough bringing machines into the equation.

“It will be a great thing for humanity when machines are as conversant in natural language as we

are,” he says – which he believes will happen before 2020. “We’re in for a big paradigm shift.”

doc.ai is doing its part to drive that shift with an advanced Natural Language Processing technology platform that aims at letting users have natural language conversations about their health via a mobile app. It depends upon advanced AI, an edge learning network, medical data forensics and the decentralized Blockchain to generate insights from combined medical data and deliver information in context to improve users’ healthcare.

The Robo Profile app isn’t about replacing doctors, he says – in fact, medical companies can use the robo-doctor to help their clients gather medical and health data that will be used for predictive modeling. De Brouwer is emphatic that he doesn’t like the word “patient”:

“It’s too enslaving, that we are powerless and need to be patient,” he says. “This is the 21st century, so let’s be participants in the healthcare process.”

Information is stored on the user’s smartphone, and updated as he or she continually interacts with it. Another point he makes is that surveys have shown that more than half of medical information is self-reported, and that a significant percentage of patients have lied at least three times to their doctors when doing so. This may have roots in embarrassment or fear. “They’re less likely to lie to their smart phone, and more likely to give voice to their deepest anxieties and concerns to their machines,” he says, which should lead to better quality information to analyze.

Bring everything to the next doctor visit to get the M.D.’s point of view about what the data and the app’s recommendations (for exercise, medications, and so on) all add up to. “It’s a new relationship between people and their doctors and machines,” he says.

Providing Data for Dollars

Potentially, there’s profit for users in providing data to such an application and network, too, as the healthcare industry at large needs as much data as possible to analyze to grow its insights, driving a sellers’ market. DeBrouwer foresees that users will be able to deploy a biowallet, so that the information stored on their devices can be sold on the NEURON Network doc.ai is creating – a Blockchain-enabled, AI-powered, decentralized, data-centric medical network that provides algorithmic models.

It’s the start of something big, according to De Brouwer:

“This might become one of the biggest industries of the 21st century,” he says. “Healthcare as a whole has an enormous need for new datasets that are enriched and come from organic sources.”

That’s compared to today, where companies such as pharmaceutical and life sciences firms typically wind up buying datasets from research organizations, and may find themselves dealing with pretty industrialized, potentially very old and possibly quite biased information. White adult males, for instance, are often the predominant subjects of research studies.

There could be great interest in industry wanting to collect data from cross-sections of individuals who have idiopathic conditions, for instance – and to pay them for helping to train Artificial Intelligence with that data. Ten percent of people in the western world suffer from some mysterious illness, he says. “If we only solve 1 percent of that idiopathic category we have done a great thing for humanity, but also generated enormously valuable data sets,” De Brouwer notes.

Health Data Contributions

The platform is designed to help make it interesting and even fun to input information that will help train the AI engine. Users start with selfies from which it can predict factors like age, height, weight, gender, BMI, and so forth, as well as pictures of the medicines in their medicine cabinet. From that the system tells the user what it thinks his or her conditions are, and can be corrected on that if necessary. It also finds health information about users from devices like Fitbits, DNA data from services like 23andme or even data like blood test results from laboratory portals, if they consent to providing their passwords. Users themselves input other information, like vaccines they’ve had, to complete the picture.

The data in the bio profiles goes to use in its predictive models. From various data sources, including what it can glean about the environments users are in based on geolocation information, it can provide advice on everything from wearing extra sunscreen that day because of the ozone situation where they live to the need for them to get more sleep or more exercise. Users can ask their own queries too, about reasons for changing glucose levels or how to get their cholesterol down in a short time, and let AI do the work of answering them.

While that’s interesting to individuals on a personal level – and of course to the healthcare industry that sees opportunity in particular communities’ datasets to reduce costs or create better targeted medications – there’s also a chance for people with other conditions to bond together to sponsor their own research trials. They can join as a group to put their data out on the market, so to speak, to data scientists who will mine that healthcare Blockchain, building models based on it and returning the results to the buyers.

As an example, De Brouwer imagines that parents of children with a condition like seizures each can contribute their child’s anonymized data and a fee to be part of a trial that could use the aggregated data to assess triggers and the performance of different types of medications and doses across segments of children in various age groups, to predict potentially better options for parents to explore for their child. If 10,000 parents join in, say, for $500 each, that’s $5 million to attract some of the finest data talent on earth to deliver the best models, he says.

Parents can take that data to their medical providers for their opinions, he says. It’s likely the case even many of them haven’t had access to results based on such a targeted set of data. “We talk to a lot of doctors and they also want to be replaced by a better version of themselves,” he says. “There are organizations of parents or patients everywhere that don’t know what to do, and they can come together now,” contributing uniformly-collected data that meets their own needs for machines to interpret rather than waiting – and hoping – that some other party will do it.

There are two pilot projects in beta leveraging doc.ai’s technology. One of them is with Deloitte Life Sciences and Healthcare and doc.ai’s Robo-Hematology solution, a conversational agent for answering questions on blood biomarkers. The NEURON network formally comes online in the next six months.


Photo Credit: Sergey Nivens/Shutterstock.com

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