Volume 24
June 2018 Edition
In fact, the learning structures are not
similar at all – neither in the process, nor
the scale.
Though AI algorithms may be
potent and sophisticated, they
still lack the suppleness of human
reasoning and deduction.
Specifically, an “intelligent” re-
sponse to an outlier – an instance of
data that is a stranger to the sta-
tistics of the training data – simply
cannot be expected from an artificial
model.
(Artificial intelligence is everywhere. Let’s look at radiology. The rapid development
of artificial narrow intelligence mostly in understanding images, text, and videos
Successful models can generalize to
will have a significant impact on radiology.)
data instances that were not present in
Nevertheless, radiologists are still responsible
the training data, but not if such new instances are very
different in their statistical nature.
and accountable for the limitations of the AI
But just because AI may not be true “intelli-
gence,” that doesn’t mean it isn’t useful.
In countless sectors, AI adds tremendous value
even without the versatility of thought exhibited by
human beings.
To cite just one example from the medical realm:
radiologists, charged with spotting disease in
CT-scans and MRIs, are these days grappling with
overwhelming workloads and lengthening hours,
a dangerous combination that leads to more
errors in image analysis.
systems they utilize, and, likewise, for patient
outcomes.
Furthermore, while AI can be employed behind
the scenes to enhance efficiency and outcomes,
it should not interfere with one crucial element
of a doctor’s work – the patient experience.
As in radiology, AI applications in other fields will pri-
marily serve to bolster, not displace, the work of human
professionals. Indeed, as more and more fields are aided
by artificial intelligence, human oversight will become
more and more imperative.
It will be humans who monitor and evaluate
However, AI algorithms have shown the ability to
the key performance indicators of AI algo-
enhance the efficiency and accuracy of radiologists’
rithms, decide how AI will be implemented in
work when performing such image analysis, enabling real-world applications without jeopardizing
them to focus in on images algorithms have flagged lives, and face accountability for how AI is
as problematic – ultimately saving lives in the process. deployed.
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