The expansion of high-throughput, data-demanding biomedical research and
technologies, like sequencing of DNA, imaging protocols, and wireless health
observing manoeuvres, has shaped the need for quality researchers to form plans for
detecting, integrating, and interpreting the major amounts of data they generate. Still, a
wide variety of mathematical methods have been premeditated to accommodate the
‘large data’ produced by such assays, and familiarities with the use of artificial
intelligence (AI) skills advise that they might be chiefly suitable. In total, the
solicitation of data-intensive biomedical skills in research education has exposed that
clinically humans differ widely at all levels, be it genetic, biochemical, physiological,
exposure, and behavioral, especially with respect to disease progression and treatment
output. This suggests that there is often a need to shape up, or ‘personalize,’ medicines
to the delicate and often complex mechanisms possessed by specific patients. Given
how significant data-intensive assays are in revealing appropriate intervention targets
and strategies for personalizing medicine, AI can play an interesting role in the
expansion of personalized medicine at all major phases of clinical development for
human beings and the implementation of new personalized health products, from
finding appropriate intervention targets to testing them for their value. The authors
describe a number of areas where AI can play a significant role in the growth of
personalized medicine, and debate that AI’s ability to spread personalized medicine
will depend judgmentally on the ways of loading, accumulating, retrieving and
eventually integrating the data that is created. Authors also share their opinions about
the limitations of countless AI techniques, as well as pondering areas for further
exploration.
Keywords: AI-based personalized medicine, Artificial Intelligence, Biomedical skills, Clinical data science, Machine learning, Precision medicine.