November 2024 SOCRA Source Journal - Journal - Page 14
Figure 2.
Number of Papers Referring to AI Applications
Number of papers (Count)
Clinical trial activity
0
5
10
15
20
25
30
Pre-clinical
Design
Recruitment
Conduct
Analysis
Figure 2 number of papers referring to AI applications.
trials, and making trial 昀椀nding
easier. In addition, wearable
gadgets and sensors powered
by AI help with better patient
monitoring throughout trials,
and AI technologies allow
for more thorough statistical
analysis, which helps with issues
like missing data and visits.
Impact on Recruitment Rates
Statistical analysis of the reviewed
literature showcases the impact
of AI on patient recruitment
rates. Evidently, the graph
presented in Figure 2 illustrates
the distribution of AI applications
across different CT activities,
emphasizing recruitment as
a key area (Zhang & DemnerFushman 2017). Notably, the
positive implications of AI on
CTs are noteworthy (Chen et
al. 2019), with the potential
to increase the chance of trial
success and governing approval.
Thus, implementation of better
protocols is expected to enhance
data range, simultaneously
reducing the burden on both
patients and trial sites.
DISCUSSION
Interpretation of Results
In this in-depth study of AI
and patient recruitment in
clinical trials (CTs), an attractive
14
story emerges, emphasizing
AI’s transformative impact
in participant engagement.
Notably, our thorough
examination of the literature
shows that over 50% of
CT publications focus on
recruiting (Vydiswaran et al.
2019; Averbuch et al. 2022).
Moreover, this frequency
indicates a strong and growing
interest in using AI to engage
and enroll trial participants,
therefore, the investigation
shows AI technologies’ diverse
roles in assessing inclusion
requirements. Evidently, AI
updates these criteria, speeding
up recruitment and ensuring a
more careful and personalized
participant selection, improving
experimental participant value
(Shivade et al. 2016). AI improves
patient appropriateness,
matching them to each clinical
trial’s needs (Kang et al. 2017);
importantly, the data show how
AI transforms trial awareness.
In addition, innovative AI
methods let trial information
reach prospective participants
with remarkable accuracy (Liu
et al. 2021; Gligorijevic et al.
2019), thus, this increased
understanding broadens and
diversi昀椀es the pool of possible
trial participants and encourages
SOCRA SOURCE © November 2024
informed decision-making.
Similarly, this investigation
revealed the purposeful use of
AI for internal clinical investigator
ranking. Without a doubt, AI
algorithms speed up investigator
rating and site start-up (Weissler
et al. 2021; Kosan et al. 2022).
Overall, operational ef昀椀ciency
improves recruiting timeframes,
solving a clinical trial obstacle,
therefore, this analysis shows
that AI is transforming clinical
trial patient recruiting. Thus, we
believe that AI is a key driver
of clinical trial methodology
innovation due to technical
advances and participantcentric approaches.
Analysis of Findings in the
Context of Existing Literature
The 昀椀ndings smoothly integrate
with the literature, demonstrating
AI’s transformational power
to change clinical trial patient
recruitment, in that, AI is often
cited as a catalyst that might
change participant involvement
patterns. Notably, AI’s ability
to combine demographic,
laboratory, and -omics data
is a major asset; hence, full
connection allows AI to perform
evaluations, enabling exact
patient matching against
eligibility requirements (Getz
