The document discusses the integration of artificial intelligence (AI) in clinical trials, highlighting its applications in patient recruitment, engagement, protocol design, clinical data management and analysis, safety monitoring, medical coding, and risk assessment. AI enhances efficiency and accuracy in these areas by utilizing predictive analytics, machine learning, and natural language processing. Examples provided illustrate how AI improves participant identification, data accuracy, real-time monitoring, and personalized patient interaction.

1. Artificial Intelligence in
Clinical Trial
Presenter:
C.S. Mujeebuddin
Founder and CEO
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2. Patient Recruitment
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 AI streamlines patient identification by analyzing diverse data
sources, improving trial enrollment accuracy and speed.
Role of AI: Utilizes predictive analytics and algorithms to identify
suitable candidates.
Example: Natural language processing (NLP) analyzes electronic
health records to find eligible participants efficiently.
10-02-2024

3. Patient Engagement
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 AI enhances patient engagement through personalized
communication, monitoring, and support, improving overall trial
experience.
Role of AI: Enhances patient participation and retention through
personalized interactions.
Example: Chatbots powered by AI provide patients with real-time
information, improving engagement and compliance.
10-02-2024

4. Protocol Design
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 AI assists in designing robust protocols by analyzing historical
trial data, optimizing criteria, and enhancing trial efficiency
Role of AI: Assists in designing optimized protocols, improving
study feasibility.
Example: AI algorithms analyze historical trial data to suggest
protocol modifications for better outcomes.
10-02-2024

5. Clinical Data Management
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 AI automates data collection, ensuring real-time monitoring for
anomalies, errors, and adherence to protocols, enhancing data
quality.
Role of AI: Enhances data accuracy and efficiency in data cleaning.
Example: Machine learning algorithms identify and rectify errors
in clinical data, reducing manual workload.
10-02-2024

6. Clinical Data Analysis
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 AI processes vast datasets, providing actionable insights for
decision-making, improving trial outcomes and efficiency.
Role of AI: Leveraging advanced algorithms to extract meaningful
insights from vast amounts of healthcare data.
Example: Medical imaging analysis, where AI algorithms can
assist radiologists in detecting abnormalities in X-rays, MRIs, and
CT scans
10-02-2024

7. Safety Monitoring
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 AI analyzes patient data to detect and predict adverse events,
enabling timely intervention and improving participant safety
Role of AI: Monitors patient safety and identifies potential issues in
real-time.
Example: AI algorithms analyze patient data to detect adverse
events, improving safety monitoring.
10-02-2024

8. Medical Coding
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 Increase in the data sources and with global sites, the
medical coding process has been facing the issue of increase
in the manual coding, against higher automated coding
when the sites were limited to a single country.
 ML models are being used in conjunction with NLP to
identify the terms reported from the existing encyclopaedia
of terms and newer terms from public domain, reducing
any manual intervention to a great extent.
10-02-2024

9. Risk assessment
10-02-2024
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 RACT has been the tool that aids in the creating of a
risk based plan. However, to devise and execute a
fully operational RACT would require the association
of various teams that include but not limited to
Investigators, CRA, CDM, Coders, Programmers, and
Statisticians.
 This in turn effects the finances and increases the
time taken for the inception of various other tasks.
 Hence, AI and specifically unsupervised models are
being used in a large extent in the preparation of a
risk plan and also identification of KRIs and QTLs.

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11. THANK
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