system can be told when it gets something right and when it gets something wrong.
The more training problems the system can chew through, the better its hit rate gets.
That’s relatively simple when it comes to training the system to identify malig-
nancies in x-rays. But for potentially groundbreaking puzzles that go well beyond
what humans already do, like detecting the relationships between gene variations
and disease, Watson has a chicken-and-egg problem: how does it train on data that
no experts have already sifted through and properly organized? “If you’re teach-
ing a self-driving car, anyone can label a tree or a sign so the system can learn to
recognize it,” says Thomas Fuchs, a computational pathologist at Memorial Sloan-
Kettering, a cancer center in New York. “But in a specialized domain in medicine,
you might need experts trained for decades to properly label the information you
feed to the computer.”
Some version of that stumbling block emerges in every domain in which IBM
hopes to have Watson contribute—as it does for any company’s machine-learning
solution. To train Watson to go through giant pools of data and pull out the few
pieces of information important to a single patient, someone has to do it by hand
first, for thousands and thousands of cases. To recognize genes linked to disease,
Watson needs thousands of records of patients who have specific diseases and
whose DNA has been analyzed. But those gene-and-patient-record combina-
tions can be hard to come by. In many cases, the data simply doesn’t exist in the
right format—or in any form at all. Or
the data may be scattered throughout
dozens of different systems, and diffi-
cult to work with.
Consider, for example, the goal of
improving primary care by placing bet-
ter data at the fingertips of clinicians.
When doctors miss chances to treat
relatively minor concerns during a rou-
tine primary-care visit, before a more
advanced problem sends patients to
an emergency room or a specialist,
their health suffers and costs explode.
“About one-third of every dollar spent on
health is probably unnecessary,” says
Anil Jain, IBM Watson Health’s chief
medical officer, who is also a practicing
primary-care physician. Machine learn-
ing is widely recognized as an opportu-
nity to address that problem. A D A