November 2024 SOCRA Source Journal - Journal - Page 12
stricter inclusion and exclusion
criteria worsen the problem by
reducing the candidate pool
(Siah et al. 2019). Distinctly,
this constraint impacts trial
participant variety and research
outcomes generalizability
(Hessler & Baringhaus 2018).
Furthermore, geographic and
logistics challenges may affect
clinical trial execution and
outcome reliability. Therefore,
overcoming these obstacles
is crucial. Thus, a full study of
these problems highlights the
need for transformational patient
recruiting tactics in clinical trials.
AI’s transformational impact
on drug development has
prompted substantial discussion
and practical research into its
possible uses in Clinical Trials
(CTs), therefore, this thorough
assessment seeks to identify
AI’s many prospects in CTs and
solve its barriers to smooth
incorporation into current
practices. The main emphasis
is on how AI, a cutting-edge
technology, may improve
patient recruitment, protocol
design, and trial success.
Unquestionably, pharmaceutical
companies worldwide are
considering the best AI
applications (Fortmeier et al.
2022), emphasizing the need
for this research. Consequently,
the 昀椀rst steps of testing AI
interventions are underway, and
regulatory agencies carefully
examine the environment to
identify areas that need norms
and frameworks (Feijoo et al.
2020). Notably, the wide range
of AI applications, with a focus
on patient recruitment and
protocol creation, highlights the
need for creative techniques
to overcome classic obstacles
(Kolla et al. 2021; Weissler et
12
al. 2021; Vazquez et al. 2021).
This investigation explores the
connections of technology,
healthcare, and regulatory
frameworks to enable AIintegrated clinical trial practices
with remarkable ef昀椀ciency
and effectiveness, whereby,
integration of AI into clinical
trials serves as a transformative
solution to overcome challenges
in patient recruitment.
Therefore, by leveraging AI
technologies, companies can
accelerate drug development,
improve protocol ef昀椀ciency,
and ultimately enhance overall
trial success rates. Thus, this
comprehensive review explores
the opportunities, challenges,
and future implications of
integrating AI into clinical trials.
METHODS
Literature Review
A comprehensive literature
review of English-only
publications was conducted
while exploring AI applications
in clinical trials, where it entailed
search into PubMed, SCOPUS,
International Pharmaceutical
Abstracts and Google Scholar
for “arti昀椀cial intelligence” or
“machine learning” and “clinical
trials.” Notably, the search
parameters were designed
to avoid irrelevant clinical
practice, surgery, diagnosis, and
treatment outcomes. By utilizing
exclusion criteria, the search was
narrowed down to concentrate
just on AI and machine learning
applications used in clinical
trials. A great deal of care
was taken in evaluating the
papers, with particular attention
paid to categorizing scienti昀椀c
activities associated with clinical
trials. This included several
phases, such as the design
of pre-clinical studies, hiring
SOCRA SOURCE © November 2024
procedures, conducting trials,
and data analysis. The main
goal was to investigate and
comprehend the use of arti昀椀cial
intelligence and its in昀氀uence on
the recruitment of patients for
clinical trials. Unquestionably,
intentional emphasis on patient
recruiting showed how AI’s
revolutionary skills enhance this
key clinical trial component.
Thus, reviews of 昀氀awless
transition from AI applications to
patient recruitment revealed how
AI has changed clinical trials.
RESEARCH DESIGN
To ensure the relevance of the
gathered information, searches
were conducted between
2016 and 2023, with results
from PubMed, SCOPUS, and
Google Scholar, as well as
regulatory documents from the
European Medicines Agency
(EMA), European Commission
(EC), and the Food and Drug
Administration (FDA), and were
compiled into a consolidated
dataset. Furthermore,
publications and documents
prior to 2016, duplicates, and
those not relevant to the EU or
US were excluded, and results
were manually condensed based
on title and abstract content.
Methodology for Data
Extraction and Analysis
Data extraction involved
categorizing publications
based on AI application areas
and therapeutic areas (TAs)
mentioned. Hence, criteria for
categorization were derived from
the research activities involved
in clinical trials, ensuring a
detailed analysis of AI’s impact
on patient recruitment. Notably,
48 publications and 9 governing
documents were included in the
review, providing an overview
