November 2024 SOCRA Source Journal - Journal - Page 16
This analysis highlights the
dif昀椀culty of creating infrastructure
and diverse knowledge to enable
emerging AI technologies.
Basically, AI incorporation into
clinical trials requires technical
capability and multidisciplinary
collaboration to provide a solid
basis (Bhattamisra et al. 2023).
Indeed, this paper acknowledges
the dif昀椀culty of this task, the
time, money, and skills needed
to seamlessly integrate AI
technologies into the clinical
trial ecosystem. Moreover, the
work shows the promise of AI to
transform patient recruiting, but
it also recommends a cautious
recognition of the obstacles.
Evidently, addressing these limits
requires ethical norms, consistent
data sharing, and a supporting
infrastructure that promotes
responsible AI advancement in
clinical studies.
FUTURE DIRECTIONS
This comprehensive study
illuminates the present state
of AI and clinical trial patient
recruiting research and suggests
possible future directions. To
begin with, as with any growing
subject, resolving restrictions
is essential to developing AI
in clinical trial methodology.
Naturally, the need for strong,
standardized databases is
urgent, because AI tools depend
on the quality and regularity
of their training data (Ye et al.
2022). Thus, future research
should emphasize creating
extensive datasets that ful昀椀ll
severe requirements. In fact,
this crucial step improves AI
application dependability and
generalizability across clinical
trial conditions, therefore, ethics
are crucial and need greater
examination (Dercle et al. 2022).
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Additionally, as CTs integrate
AI, ethical usage and regulation
of AI technologies are crucial,
hence, future studies should
examine how to handle personal
patient data, mitigate biases,
and navigate healthcare AI legal
frameworks. In brief, this ethical
compass will help build strong
regulatory frameworks and AIintegrated healthcare practices
that preserve core values.
The use of AI in virtual control
arms is exciting for future
research, therefore, the 昀椀ndings
suggest that AI might predict
illness development in virtual
control arms, changing trial
design (Askin et al. 2023).
Notably, this new technique
requires validation procedures
and more study to demonstrate
AI-generated data’s regulatory
dependability and acceptability.
Unquestionably, AI’s effect on
trial success is important to
study, thus, future study should
examine protocol design,
clinical outcomes, interventional
arms, and eligibility criteria to
determine trial success and
failure. Still, researchers may
estimate regulatory approval and
clinical trial success probability
using predictive models
based on clinical trial design
and patient characteristics,
hence, collaboration to
create methods for massive
datasets is key to future study.
Nevertheless, standardizing
data collecting, preservation,
and organizing frameworks
reduces AI output mistakes, in
this case, AI methods for illness
progression prediction in virtual
control arms seem promising
(Aldoseri et al. 2023). Overall,
to address obstacles and ethical
concerns, harnessing these
SOCRA SOURCE © November 2024
breakthrough technologies
requires broad knowledge and
collaboration. Notably, this
thorough assessment supports
AI’s transformational potential
in clinical trial patient recruiting
and calls for a forward-looking
strategy, therefore, the study
provides the framework for
a future where collaborative
research and continuing
discovery unleash AI’s full
potential, enabling patientcentric clinical trial settings.
CONCLUSION
This comprehensive
review has examined how
Arti昀椀cial Intelligence (AI) is
changing clinical trial patient
recruiting while solving old
pharmaceutical problems.
Essentially, clinical trials as
the foundation of medical
development, have struggled
with participant involvement.
Notably, inef昀椀ciencies,
delayed enrollment rates, and
diversity issues hinder drug
development in traditional
techniques, however, the results
demonstrate AI’s potential to
transform clinical trial patient
recruiting. Evidently, over 50%
of the studied literature focuses
on recruiting, demonstrating
that the scienti昀椀c community
may view AI as a revolutionary
tool in drug development.
Furthermore, AI applications
like automated eligibility
analysis and trial awareness
signify a pattern change
in clinical trial design and
execution. Hence, in highimpact therapeutic 昀椀elds like
oncology, statistical analysis will
show how AI affects recruiting
rates. Thus, AI applications
may boost patient recruitment,
which tackles slow enrollment
