Summer 2021 Publication

SAFE AND RESPONSIBLE AI
FOR HEALTHCARE
ALYSSA TANG
F
eaturing Andrew Ng, Fei-Fei Li and
other renowned leaders in the field, the
AIMI Symposium focuses on the latest
research on the role of AI in diagnostic
and clinical implementation including the
societal impact of its use.
As one of the speakers at the virtual
symposium hosted by Stanford Center for
Artificial Intelligence in Medicine and
Imaging (AIMI) held on August 3, 2021,
Fries addressed the shortcomings as well
as advancements in machine learning and
artificial intelligence in areas of
medicine.
“There have been a lot of recent
publications on the proliferation of
COVID models but they haven’t made any
contribution to the value in healthcare,”
said Dr. Jason Fries, a research scientist
at Stanford Center for Biomedical
Informatics Research.
Fries attributes the aforementioned
problem to so-called “Frankenstein
datasets” citing “Hundreds of AI tools
have been built to catch COVID. None of
them helped,” published in the MIT
Technology Review. Such datasets of
scans and electronic health records were
often “spliced together from multiple
sources and can contain duplicates,”
according to the July 30, 2021 MIT
article. Researchers discovered that
algorithms trained on a combination of
scans of people lying down and standing
up were determining the risk of
contracting COVID based on their
position rather than the actual features of
the scan. This likely resulted from the
confounding factor that people lying
down had more serious conditions than
people standing up. Other models
incorrectly associated the scan label fonts
that hospitals with higher cases used
with greater risk for the disease. Fries
elaborated on the need for data-centric
AI to solve these issues presented by the
pioneer of machine learning, Professor
Andrew Ng. In his introductory remarks,
Ng emphasized the necessity of shifting
from model-centric to data-centric AI for
success in the medical space.
(Image Credit: IT Chronicles)
As the founder of DeepLearning.AI and
adjunct computer science professor, Ng
believes that “For many years, we’ve
known that healthcare and AI holds a lot
of promise. AI will - or at least
supposedly will - transform healthcare,”
Ng said. Despite the immense research
progress in AI and healthcare in the last
decade, significant work is needed to put
these algorithms into production in a
“safe and responsible way.”
“A lot of applications have a proof of
concept to production gap,” according to
Ng. Frequently, researchers publish
papers touting high diagnostic accuracy
on test sets, sometimes with comparable
or even better results than clinicians, yet
these promising algorithms are not
utilized in practical healthcare settings.
Ng said, “the gap is getting the
sufficiently high quality data to feed to
the neural network to get you the
performance you need to deploy these
systems.” The key is to invent the tools
and techniques to be more systematic
about entering the data. He also
EUNOIA GLOBAL HEALTH 41