following 5 years. Clinicians should be convinced that the new technology will
offer tangible results. It is important to prove clinical applicability, value, and
relevance of all new technologies; identify stable biomarkers; validate known
biomarkers; and confirm their application in the risk assessment and prediction of
outcome. Diagnostic tests for preselective screening should be 100 % reliable. It
will be indispensable to prove to health professionals the clinical benefit of new
biomarkers, in particular in asymptomatic, apparently healthy subjects. There is
also a possibility of inflating the risk of overdiagnostics and false positive results. It
is necessary to forward particular technological steps and interpretations such as
exploitation of biobanks and clinical sample collection program so as to ensure
global approach to technological support and infrastructure (path to integrated
model, including central reference database). Perhaps in this context of proof of
principle the focus should be placed on some specific diseases such as diabetes,
asthma, rheumatoid arthritis, cardiometabolic diseases or on some subgroups, e.g.,
nonsmall cell lung carcinoma, where tangible results could be observed in a short
period.^16
One can presume that implementation of personalized medicine will last for
20 years. In the first 5 years, it will be necessary to confirm the personalized-omics
proof of principle. Determination of genomic, transcriptomic, proteomic,
metabolomics and auto-antibody profiles means that the same type of analysis
will be carried out several times in the same individual. The generation of dynamic
integrative personalized-omics profile (iPOP) will ensue; destiny of personalized
medicine is an emphasis on individual data rather than on an average population
data. Important data obtained individually can be lost or masked in population
study. iPOP can serve to guide lifestyle changes in order to prevent disease. It will
be necessary to address the issues such as data interpretability, patient’s choice,
privacy, ethical usage of personalized data, etc.
In the next 10 years, one could expect that health professionals would be more
eager to accept and support personalized approach once the technology confirms
clinical value. It is a must to develop algorithms based on the interaction between
different -omics and environmental data (e.g., lifestyle). These data would be
integrated with those obtained by molecular imaging methods. Technology would
have to be developed and adapted for a lifetime monitoring of individual health.
Long-term vision means the creation of personalized database that will accompany
each individual from birth (and even prenatal), taking also into account geograph-
ical locations in which this person resides. This also implies management of data
that are sensitive and personal, construction of sensor for real-time data monitoring
for each subject, the matter of exchange and limitation of those personalized data.
One of the greatest challenges would be translation from “bench to bedside.”
When personalized medicine enters clinics, technological requirements would
be to increase preciseness and reduce the time necessary to respond. Molecular
imaging technologies may possibly undergo changes. The current problem lies in
(^16) Walker and Mouton ( 2006 ), Pavelic ́et al. ( 2015 ), and Kraljevic ́et al. ( 2004 ).
10 K. Pavelic ́et al.