IEEE Spectrum June, 2015 - 77

Special RepoRt

that broad field, to specific disciplines and tasks. Efforts to do so
have spawned many different medical Watsons. Some of the first could
be found at the Cleveland Clinic,
Memorial Sloan Kettering Cancer
Center, MD Anderson Cancer Center,
and insurance company WellPoint

(now called Anthem), each of which
started working with IBM to develop
its own health-care-adapted version
of Watson about three years ago. Two
years later, as the hardware shrank
from room-size to small enough for
a server rack, another round of companies signed on to collaborate with
IBM. Among these are Welltok, makers of personal health advisory software; @Point of Care, which is trying
to customize treatments for multiple
sclerosis; and Modernizing Medicine,
which uses Watson to analyze hundreds of thousands of patient records
and build treatment models so doctors can see how similar cases have
been handled.
Watson's training has been slow
going, especially as each iteration
needs to be tested with new questions. And Watson's text-processing
powers, which might seem to make
it an ideal tool for handling the rapid
growth of medical literature, have
proved to be of limited value. In many
cases, as when modeling the decisions of top lung-cancer specialists
at Memorial Sloan Kettering, there
aren't yet journal articles or clinical
guidelines with the right answers.
A not her i s sue i s d at a qu a l it y.
WatsonPaths, which Mehta has been
developing at the Cleveland Clinic,
is the closest thing yet to that archetypal Dr. Watson, but it can work only
if the AI can make sense of a patient's
records. As of now, electronic medical records are often an arcane collection of error-riddled data originally
structured with hospital administration in mind rather than patient care.
while its "Jeopardy!" triuMph

was "a great shot in the arm" for
the field, says Mark Musen, a professor of medical informatics at Stanford,
IBM is just one of many companies and
institutions in the medical-AI space.

Hacking the Human oS

Take the AI that Massachusetts General Hospital developed called QPID
(Queriable Patient Inference Dossier),
which analyzes medical records and
was used in more than 3.5 million
patient encounters last year. Diagnostic programs like DXplain and Isabel are
already endorsed by the American Medical Association, and startup company
Enlitic is working on its own diagnostics. The American Society of Clinical
Oncology built its big-data-informed
CancerLinQ program as a demonstration of what the Institute of Medicine,
part of the U.S. National Academies,
called a "learning health system." Former Watson developer Marty Kohn is
now at Sentrian, designing programs
to analyze data generated from homebased health-monitoring apps.
Meanwhile, IBM is making its own
improvements. In addition to refinements in learning techniques, Watson's programmers have recently
adde d sp e e c h re co g n it ion a nd
visual-pattern analysis to their toolbox. Future versions might, like the
fictional HAL 9000 of sci-fi fame, see
and hear. They might also collaborate
if innovations in individual deployments are eventually shared.
How will all this shake out? When
will AI transform medicine, or at least
help improve it in significant ways? It's
too soon to say. Medical AI is about
where personal computers were in the
1970s, when IBM was just beginning to
work on desktop computers, Bill Gates
was writing Altair BASIC, and a couple of guys named Steve were messing around in a California garage. The
application of artificial intelligence to
health care will, similarly, take years
to mature. But it could blossom into
something big. n
For an extended version of this
article or to post your comments, go to
http://spectrum.ieee.org/drwatson0615

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Table of Contents for the Digital Edition of IEEE Spectrum June, 2015