By Daniela Hernandez and Asa Fitch
Ten years ago, International Business Machine Corp.'s artificial
intelligence system Watson bested humans at the quiz show
"Jeopardy!"
The feat was supposed to herald a shift in the way machines
served up answers to questions big and small, opening up new
revenue streams for Big Blue specifically and Big Tech more
generally. A key target: healthcare, a trillion-dollar industry
many say is saddled with inefficiencies that some tech advocates
say AI could cure.
A decade later, reality has fallen short of that promise. IBM is
now exploring the sale of Watson Health, a unit whose marquee
product was supposed to help doctors diagnose and cure cancer.
IBM spent several billion dollars on acquisitions to build up
Watson. Former senior IBM executive John Kelly once touted the
initiative as a "bet the ranch" move. It didn't live up to the
hype. Watson Health has struggled for market share in the U.S. and
abroad and currently isn't profitable.
Alphabet Inc.'s Google DeepMind unit, which famously developed a
Go-playing algorithm that vanquished a champion human player in
2016, later launched several healthcare-related initiatives focused
on chronic conditions. It also has lost money in recent years and
run into privacy concerns over how health data was being
collected.
The stumbles highlight the challenges of attempting to apply AI
to treating complex medical conditions, healthcare experts said.
The hurdles include human, financial and technological barriers,
they said. Having access to data that represents patient
populations broadly has been a challenge, the experts say, as have
gaps in knowledge about complex diseases whose outcomes often
depend on many factors that may not be fully captured in clinical
databases.
Tech companies also sometimes lack deep expertise in how
healthcare works, adding to the challenge of implementing AI in
patient settings, according to Thomas J. Fuchs, Mount Sinai Health
System's dean of artificial intelligence and human health.
"You truly have to understand the clinical workflow in the
trenches," he said. "You have to understand where you can insert AI
and where it can be helpful" without slowing things down in the
clinic.
For IBM, the retreat underscores the difficulties new CEO Arvind
Krishna faces in restoring growth at the iconic tech company. Mr.
Krishna has said AI, along with cloud-computing, would be pivotal
for IBM's prospects.
Watson Health was one of IBM's first and the largest AI efforts,
said Toni Sacconaghi, an analyst at Bernstein Research. IBM
initially promoted it as an engine for growth, but more recently
has given it less prominence amid mounting business struggles,
leadership changes and layoffs, he said.
"Watson may be very emblematic of a broader issue at IBM of
taking good science and finding a way to make it commercially
relevant," Mr. Sacconaghi said.
Even as Watson Health ran into problems, the company's research
arm has continued to give priority to AI and healthcare. IBM
Research and Pfizer developed speech tests last year to predict the
onset of Alzheimer's disease, the company said last year.
IBM wouldn't comment about the sale, but said Watson Health has
had successes over the years. "This work began nearly 10 years ago,
at the beginning of the AI revolution, and we explored
groundbreaking space in helping physicians advance healthcare
through AI," the company said. "IBM is continuing to evolve the
Watson Health business, based on our decade of experience, to meet
the needs of patients and physicians."
A sale would mark Mr. Krishna's second major move to exit
struggling businesses in less than a year at the helm. IBM last
year said it planned to spin off its managed IT services division,
which generated about $19 billion of annual revenue, or about a
quarter of its total sales.
By slimming IBM down, Mr. Krishna expects IBM to deliver
consistent mid-single-digit growth following a decade filled with
revenue declines. IBM had $73.6 billion in sales last year, down
from almost $100 billion in 2010.
IBM's climb down also serves as a warning to the wider tech
industry that sees healthcare as a promising growth market. Watson
Health and some other tech-industry AI projects that have struggled
were overly ambitious, trying to answer broad, complicated
health-related questions, experts said. Watson Health, for
instance, was marketed broadly as finding answers to all kinds of
cancer, they said.
"When the notion is, 'Well, we can answer any question in cancer
care with this data,' it's too overwhelming. We don't have the
power to do that right now," said David Agus, the chief executive
of the Ellison Institute for Transformative Medicine at the
University of Southern California and an early tester of the Watson
system.
Another challenge is the lack of data-collection standards,
which makes taking an algorithm that was developed in one setting
and applying it in others difficult, experts said. "The
customization problem is severe in healthcare," said Andrew Ng, an
AI expert and CEO of startup Landing AI, based in Palo Alto,
Calif.
The most successful applications of AI in healthcare to date
have been when the technology aims to solve discrete and narrow
problems, according to Cynthia Burghard, research director at IDC
Health Insights, a technology market research and advisory services
firm. Such applications include alert systems that warn doctors
which of their patients might be at risk for readmissions or severe
outcomes and chatbots that help answer basic questions.
Recently, some healthcare providers and insurers also have
married different data sources, including medical history and
income-related information, to come up with risk scores for
patients to identify those potentially more vulnerable to Covid-19
exposure to target outreach to them, she said. Such applications
are easier to manage because they don't involve diagnoses.
Other areas where AI has seen some successes include radiology
and pathology, disciplines where image-recognition software can be
applied to answer specific questions, experts said.
"It's about incremental improvements. It's not about solving the
most complex things in healthcare," she said. "We might get there
someday, but [right now] it's crawl, walk, run."
Another area where the technology has had inroads is in
streamlining business processes, like billing and charting, rather
than in making diagnoses, experts said, because the stakes are
lower, and there is better data to make these systems work. There
are also clear financial incentives, they said.
"There's a lot of human capital invested in these things, and a
lot of that could be markedly reduced with AI support." said Eric
Topol, a cardiologist and executive vice president at Scripps
Research.
Despite the challenges of applying AI in healthcare, experts
said they expect investments to continue.
"The market size is infinite," said USC's Dr. Agus. "Healthcare
is probably a trillion-dollar market and it's probably 40% to 60%
inefficient. So the notion that you can make it dramatically better
with something as elegant as a machine-learning algorithm, or AI,
which is scalable, obviously is very enticing."
Write to Daniela Hernandez at daniela.hernandez@wsj.com and Asa
Fitch at asa.fitch@wsj.com
(END) Dow Jones Newswires
February 20, 2021 12:01 ET (17:01 GMT)
Copyright (c) 2021 Dow Jones & Company, Inc.
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