It’s inevitable.
A.I is going to change healthcare as we know
it.
But in this video, I want to show you a few
medical specialties where it’s already making
a difference.
Radiology
In 2018, a deep-learning-based algorithm was
developed using more than 50 thousand normal
chest images and almost 7 thousand scans with
active TB.
The algorithm became so good that in performance
tests it easily beat radiologists.
Of course, it has its shortcomings, but this
test shows that even the A.I of today can
be a helpful second reader for physicians,
while the A.I. of tomorrow can bring screenings
and precise diagnostics to even less developed
and rural areas where medical professionals
are not available.
Dermatology
A.I is advancing elsewhere too.
Researchers in Germany, the US and France
trained a deep learning neural network to
identify skin cancer by feeding it with more
than 100,000 images of malignant melanomas
and benign moles.
After its training, they compared its performance
with 58 international dermatologists and the
results were remarkable.
While the dermatologists accurately detected
more than 86% of the melanomas, the neural
network detected 95% of them.
Oncology
One of the biggest promises of A.I. is that
one day it could crack the code of individualized
cancer diagnosis and treatment.
And Watson, IBM’s very own A.I, is a powerful
tool that is mainly being used and tested
in the field of Oncology.
So far, dozens of hospitals have adopted this
technology and it’s been used in conjunction
with medical judgment.
While its promise is strong, it has not yet
been able to live up to the expectations in
the fight against cancer.
Cardiology
Cardiovascular diseases are the number one
cause of death globally.
For those affected, early detection is critical
for both management and treatment.
And in the future, A.I. based predictions
could be a life-saver.
Since studies have shown that markers of cardiovascular
disease can often manifest in the eye, scientists
are using deep-learning methods to identify
risk factors such as age, gender, smoking
status, and blood pressure only by looking
at the eye.
These new studies still need to be validated
and repeated on more people before they could
gain broader acceptance, but since retinal
images can be obtained quickly, cheaply and
non-invasively, this will probably open new
horizons in healthcare.
A.I. also has several limitations.
Most of these studies have not been tested
under clinical circumstances and algorithms
are only precise in a specific task while
clinical life is much more diverse.
Nevertheless, what matters here is that A.I.
has amazing promise.
