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Exclusive: Watson is like a 'well-read medical student'

by Brendon Nafziger , DOTmed News Associate Editor

In what ways would this apply to imaging?

Well, what I really would like to do is start looking at how, from the medical imaging perspective, we might be able to extract important and relevant information from a patient's chart, for radiologists who need to see a synthesis or summary of what are the important and pertinent details. The history that we typically get is fairly limited. It might say a patient has a new fever, but what they don't tell us when they request the study, is that the patient has lymphoma and is HIV positive, and has had a recurring history of bouts with pneumonia, for example, and all the other things that would be really important for us to help make a diagnosis.
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So what I'm really interested in from the imaging perspective as an initial step, is to simulate or emulate what in an academic setting my residents and fellows do currently - by spending a half an hour reading through the medical charts, talking with the patient, gathering information, looking at previous studies, collating all that knowledge and then presenting it to me in such a way that I can be more accurate and more efficient in making a diagnosis.

So you sort of foresee a DeepQA technology in radiology, at least at first, acting as sort of a synopsis-maker - giving the doctor a quick capsule medical history of the patient?

One of the things I'm going to show at the Dwyer lecture is a kind of a timeline where there are three pictures of a stick drawing of a patient, and some of this concept was originated at Massachusetts General Hospital by Dr. Supriya Gupta. She essentially thought of the concept of having a little graphical stick figure, and on that stick figure you can create systems drawings. You can create a lung nodule, or a brain tumor or a myocardial infarction, and then from one image to another, you can see whether the tumor's getting bigger or smaller, whether that problems' gone away, and so in maybe five or 10 seconds, glancing at three pictures, I can essentially have a summary that I can drill into if I want to, that so much supplements my just looking at a new imaging study cold.

Just the creation of that synopsis itself could change the way we practice radiology to a substantial degree by making important and relevant data available to the radiologist routinely. And I just see that as phase one.

What are the next phases?

At the VA there is a data warehouse of structured and unstructured information on all patients that goes back more than 12 years. So now the VA has billions of transactions or bits of data on patients, that there's the capability to mine, either in real-time or preprocessed. So step two after being able to synthesize the data and summarize it and create those synopses, is actually to have the computer make suggestions. The first small step would be to have the computer look at discrepancies. To say, hey, here's a problem list, where it says the patient is diabetic, but I can't find any recent examples of the patient's glucose being abnormal. Or here you have the patient essentially diagnosed with having myocardial ischemia, but all of the cardiac studies that have been done seem to suggest that's not the case...

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