- Lung cancer has the highest rate
of mortality among all cancers.
Accounting for an estimated 160,000 deaths
in the United States in 2018.
Early detection has been shown
to reduce mortality by 20 to 43%.
That's why medical guidelines endorse
the use of low dose CT imaging for lung cancer screening.
Despite the guidelines, even in a rich economy
such as the United States, the healthcare system
suffers from challenges.
Radiologists are prone to erroneous conclusions.
And there is significant variability
in the skill levels and findings across radiologists.
Emerging economies suffer from additional problems.
The shortage of trained radiologists
far exceeds the shortage of imaging centers.
In May 2019 scientists at Northwestern University
and Google co-authored a paper in "Nature Medicine"
detailing the results of tests
with an artificial intelligence system trained
to read low dose CT images.
The scientists also compared the accuracy
of the AI systems diagnoses with those
of six expert radiologists.
This is a leading example of the application
of AI to computer vision.
In cases where they could look at both
the recent scan and an earlier one
the AI system performed as well as the radiologists.
However, if both looked at only the more recent scan
the AI system beat the radiologists.
The AI system also produced fewer false positives,
that is incorrectly concluding the presence of cancer.
As well was fewer false negatives.
That is incorrectly concluding its absence.
Thereby reducing unnecessary follow up procedures
and cases of missed tumor.
Additionally, the system was able
to pinpoint regions of the lung which carried
a higher risk of malignancy.
So, what were the sources of the AI system's advantages?
First, the system was not bound by the constraints
of the human eye.
Which can look at only 2D image slices on the screen.
Instead, the system could look at
the entire 3D image in one go.
In fact, because the system could look at both
the recent and the earlier 3D images simultaneously,
it really had 4D vision with time as the fourth dimension.
Second, because of it's far greater computational power
the system could analyze each pixel
as well as various combinations of pixels
with far greater granularity than human radiologists could.
Third, the system relied on deep learning.
A more recent branch of AI.
In deep learning, the AI is trained on a subset of cases
where it is given anonymized scans
and is told only whether or not later tests would reveal
the presence or absence of cancer.
With no prior medical expertise coded in
the system had to learn on its own
how to make better predictions about
the presence or absence of cancer through
a rapid fire sequence of trials and errors.
This is a process very much akin to how a child learns
how to crawl, stand up, walk, and run.
This blank slate approach made the system open
to the discovery of novel signals of cancer
in the CT scans.
As a result, the AI system can now begin
to teach human radiologists, an interesting case
of a reverse learning.
The utility of these type of system should
be even greater in emerging economies
as it can help fill in the gap caused
by the scarcity of trained radiologists.