November 2024 SOCRA Source Journal - Journal - Page 15
et al. 2020). In addition, AI’s
capacity to navigate and
analyze varied information
helps overcome the challenges
of 昀椀nding quali昀椀ed trial
participants (Koesmahargyo
et al. 2020; Mayorga-Ruiz et
al. 2019; Goldstein & Rigdon
2019). Obviously, the results
highlight the symbiotic
interaction between structured
data and Natural Language
Processing (NLP) in eligibility
screening. Essentially, NLP
helps AI understand language’s
unstructured text while creating
organized facts (Zhou &
Manser 2020). Therefore, this
interaction improves eligibility
and screening ef昀椀ciency while
emphasizing the signi昀椀cance
of standardizing eligibility
criteria (Haddad et al. 2021;
Banerjee et al. 2022). In brief,
the cooperation of AI’s data
integration, accurate matching,
and powerful language
processing might transform
patient recruiting. Thus, AI is
crucial to a new age of ef昀椀ciency
and superiority in clinical trial
recruiting, as the reviewed
literature has shown.
IMPLICATIONS
Integrating AI into clinical
trial patient recruiting has
transformational effects, similarly,
this review highlights AI’s
crucial role in improving trial
access and equity. Furthermore,
the synthesized information
shows how AI democratized
clinical trial access, a subject
overwhelmed by awareness
and inclusion issues, hence,
AI can considerably increase
trial access (Krittanawong et
al. 2019; Harrer et al. 2019).
Notably, AI improves public
Clinical Trial (CT) platforms
by using large datasets and
advanced algorithms to create
a more informed and accessible
environment (Delso et al. 2021).
Unquestionably, this increases
the exposure of current studies
and empowers a wider public
with knowledge, breaking down
barriers to varied participation,
thus, AI in patient recruiting
makes trial participation fairer
(Ismail et al. 2023). In addition,
integrating demographic,
laboratory, imaging, and data
allows more detailed matching
(van der Lee & Swen 2023),
hence, this integration allows
more careful matching of
possible trial participants with
the typically inclusion criteria.
Therefore, a more open and
varied participant pool creates a
trial atmosphere that is re昀氀ective
of society, hence, the bene昀椀ts
go beyond accessibility and
inclusion to participant safety
(Shah et al. 2022). In brief, AI
matches potential participants
more correctly with trial
parameters and streamlines
eligibility screening, thus, this
twofold effect improves clinical
trial safety by ensuring that
participants are suitable for the
protocol’s expectations and
dangers.
AI also reduces clinical trial
sponsors’ and investigators’
operational load, indeed, AI
speeds site start-up, investigator
rating and eligibility screening
by automating and optimizing
patient recruitment. Evidently,
this operational ef昀椀ciency
streamlines and lowers clinical
trial costs while reducing
recruiting time and resources
(Cowan et al. 2022). In a larger
perspective, AI may accelerate
trial times and transform
clinical trials, while AI-driven
methods reduce trial lengths,
possibly speeding medication
development and regulatory
clearance (Piette et al. 2022).
Obviously, acceleration is
essential for urgent healthcare
requirements and introducing
new therapies faster (MartiBonmati et al. 2022). Therefore,
AI in clinical trial patient
recruitment has far-reaching
effects on accessibility,
inclusiveness, participant safety,
operational ef昀椀ciency, and
trial schedule acceleration.
Thus, AI promises to make
clinical trials more accessible,
participant-centric, and ef昀椀cient
in increasing medical knowledge
and therapies.
LIMITATIONS
To demonstrate the
transformational potential of
Arti昀椀cial Intelligence (AI) in
clinical trial patient recruitment,
the work recognizes and
overcomes many constraints
that temper hope. First,
the lack of consistent and
comprehensive datasets is the
biggest restriction, whereby,
this de昀椀ciency limits AI uses,
especially in CT areas (Park et al.
2022). In addition, AI algorithms’
reach and usefulness are limited
by the lack of comprehensive
and consistent datasets, notably
in CT research, hence, the work
illuminates ethical issues that
limit AI use in clinical studies.
Notably, the biggest issue
is health information abuse,
underscoring the tight balance
between using new technology
to attract patients and protecting
sensitive health data. The ethical
issue requires strict controls and
regulatory frameworks to enable
responsible and transparent AI
usage in clinical trials.
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