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Sports betting. Tokyo Olympics. Our mission statement Arrow. About About Axios. Advertise with us. Axios on HBO. It was a single user system - had three quizmasters put three answers to it, it would have thrown the machine into a spin. Watson had to be retooled for a scenario where tens, hundreds, however many, clinicians would be asking questions at once, and not single questions either - complex conversation with several related queries one after the other, all asked in non-standard formats.
And, of course, there was the English language itself with all its messy complexity. What we inherited was the core engine, and we said 'Okay, let's build a new thing that does all sort of things the original Jeopardy system wasn't required to do'.
To get Watson from Jeopardy to oncology, there were three processes that the Watson team went through: content adaptation, training adaptation, and functional adaptation - or, to put it another way, feeding it medical information and having it weighted appropriately; testing it out with some practice questions; then making any technical adjustments needed - tweaking taxonomies, for example.
The content adaptation for healthcare followed the same path as getting Watson up to speed for the quiz show: feed it information, show it what right looks like, then let it guess what right looks like and correct it if it's wrong.
In Jeopardy, that meant feeding it with thousands of question and answer pairs from the show, and then demonstrating what a right response looked like. Then it was given just the answers, and asked to come up with the questions. When it went wrong, it was corrected.
Through machine learning, it would begin to get a handle on this answer-question thing, and modify its algorithms accordingly. Some data came from what IBM describes as a Jeopardy-like game called Doctor's Dilemma, whose questions include 'the syndrome characterized by joint pain, abdominal pain, palpable purpura, and a nephritic sediment?
The training, says Kohn, "is an ongoing process, and Watson is rapidly improving its ability to make reasonable recommendations the oncologists think are helpful. By , there were two healthcare organisations that had started piloting Watson.
Wellpoint, one of the US biggest insurers, was one of the pair of companies that helped define the application of Watson in health. And it was this relationship that helped spur Watson's first commercial move into working in the field of cancer therapies. While using Watson as a diagnosis tool might be its most obvious application in healthcare, using it to assist in choosing the right therapy for a cancer patient made even more sense.
MSKCC was a tertiary referral centre - by the time patients arrived, they already had their diagnosis. So Watson was destined first to be an oncologist's assistant, digesting reams of data - MSKCC's own, medical journals, articles, patients notes and more - along with patients' preferences to come up with suggestions for treatment options. Each would be weighted accordingly, depending on how relevant Watson calculated they were. Unlike its Jeopardy counterpart, healthcare Watson also has the ability to go online - not all its data has to be stored.
And while Watson had two million pages of medical data from , sources to swallow, it could still make use of the general knowledge garnered for Jeopardy - details from Wikipedia, for example.
What it doesn't use, however, is the Urban Dictionary. Fed into Watson late last year, it was reportedly removed after answering a researcher's query with the word "bullshit". As such, the sources are now medical publications like Nature and the British Medical Journal.
And there are other safety nets too. The doctor and a data scientist are sitting next to each other, correcting Watson. Spurious material, or conflicted material or something from a pharmaceutical company that the doctor feels may be biased - that is caught during the training cycle," added Saxena. WellPoint and MSKCC used Watson as the basis for systems that could read and understand volumes of medical literature and other information - patients' treatment and family histories, for example, as well as clinical trials and articles in medical journals - to assist oncologists by recommending courses of treatment.
Interactive Care Insights for Oncology provides suggestions for treatment plans for lung cancer patients, while New WellPoint Interactive Care Guide and Interactive Care Reviewer reviews clinicians' suggested treatments against their patients' plans and is expected to be in use at 1, healthcare providers this year. Watson has bigger ambitions than a clinician's assistant, however.
Its medical knowledge is around that of a first year medical student, according to IBM, and the company hopes to have Watson pass the general medical licensing board exams in the not too distant future.
We're starting with cancer and we will soon add diabetes, cardiology, mental health, other chronic diseases. And then our work is on the payment side, where we are streamlining the authorisation and approval process between hospitals, clinics and insurance companies," Saxena said.
The ultimate aim for Watson is to be an aid to diagnosis - rather than just suggesting treatments for cancer, as it does today, it could assist doctors in identifying the diseases that bring people to the clinics in the first place.
Before then, there is work to be done. While big data vendors often trumpet the growth of unstructured data and the abandoning of relational databases, for Watson, it's these older sources of data that present more of a problem. Watson does not process structured data directly and it doesn't interpret images. It can interpret the report attached to an image, but not the image itself. In addition, IBM is working on creating a broader healthcare offering that will take it beyond its oncology roots.
We're using it as a learning process to create algorithms and methodologies that would be readily generalisable to any area of healthcare. They don't have to have to say, right, we have oncology under control, now let's start again with family practice or cardiology," Kohn said. Watson has also already found some interest in banking. Citi is using Watson to improve customer experience with the bank and create new services. It's easy to see how Watson could be put to use, say, deciding whether a borderline-risk business customer is likely to repay the loan they've applied for, or used to pick out cases of fraud or identity theft before customers may be aware they're happening.
Citi is still early in its Watson experiments. A spokeswoman said the company is currently just "exploring use cases". For more information on the categories of personal information we collect and the purposes we use them for, please view our Notice at Collection. Become a Member Sign In. General Newsletters Got a news tip? Free: Join the VentureBeat Community for access to 3 premium posts and unlimited videos per month.
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