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can use AI to process vast digital libraries of data
(such as analysing the properties of thousands of
pharmaceutical compounds) with significantly
more accuracy than manual processing, to arrive
at potential treatment candidates. AI can also be
used in these stages for DNA sequencing based
on complex human data, allowing clinicians to
conduct tests in genetic matching and immunity
response.
Clinical development and trials
Once suitable compounds have been identified,
the process moves towards live testing. Different
patients will react differently to treatments based
on factors such as age and prior medical history.
The tests therefore need to be comprehensive
enough to cover marginal cases where a patient
may react badly to treatment.
By training deep learning algorithms, researchers
can conduct these tests at a previouslyunimaginable
scale, even before physically
administering the vaccine candidate to test patients.
These algorithms can be used to identify and
sample antibodies to fight infectious diseases with
drastic improvements in speed and cost. Advanced
analytics and data visualisation of human response
to the potential vaccines can then be used to assist
with rapid testing, allowing for more intricate
analysis and lower error rates.
Manufacturing and QA
Upon regulatory approval of vaccine products, the
race is on to develop and distribute the medicine
across a vast network of hospitals and clinics. This
has significant operational implications for the
manufacturers making the products, requiring
rapid decision-making on factors such as their
output capacity, the quality of the product and
optimum packaging solutions.
Combining AI and sensor-based technologies,
manufacturers can harness granular data to bring
greater supply chain efficiencies. This helps to avoid
demand-supply misalignments in their production
processes, and minimises the risk of products being
spoiled in distribution.
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