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Certificate Program

AI & ML for Clinical Research

Program Objective

This course introduces the fundamentals and applications of Artificial Intelligence (AI) and Machine Learning (ML) in the context of clinical research and drug development. Learners will gain an understanding of how AI/ML tools are used in protocol design, patient recruitment, clinical data analysis, risk monitoring, pharmacovigilance, and real-world evidence generation. The course balances theoretical concepts with hands-on activities using real datasets and open-source tools, preparing learners to contribute to digital transformation in clinical trials.

Mode: Online (Instructor-led / Self-paced)
Duration: 36 Hours (6 Weeks, 6 hours per week)
Certificate: Issued by IDDCR Global Institute

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Designed For

  • Clinical research professionals (CRAs, CDMs, Biostatisticians)

  • Data scientists and AI/ML engineers entering healthcare

  • Life Sciences, Pharmacy, Biotech, and Medical graduates

  • CRO, Pharma, and Healthcare IT employees

  • Students seeking a career in clinical data science or digital health

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Pre-requisite

  • Basic understanding of clinical trials and healthcare data

  • Familiarity with data analysis concepts

  • Programming exposure (preferably Python or R) is beneficial but not mandatory

  • Willingness to learn AI/ML workflows applied to real-world clinical problems

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Learning Delivery

  • Combination of recorded lectures, live Q&A sessions, and practical demos

  • LMS access to slides, code templates, datasets, and documentation

  • Weekly support forums and instructor interaction

  • Project review and feedback sessions

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Tools & Technologies Used

  • Python (Scikit-learn, Pandas, Matplotlib)

  • Jupyter Notebook

  • NLP Libraries (e.g., spaCy, NLTK)

  • Sample clinical datasets (public or anonymized)

  • (Optional) AutoML tools like Google AutoML, DataRobot

  • AI dashboards and healthcare AI case studies

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Assessment & Certification

  • Quiz after each module

  • Practical assignments and use-case analysis

  • Final capstone project submission

  • Certificate of Completion (minimum 70% score required)

  • Certificate of Completion issued by IDDCR Global Institute

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Learning Outcome

By the end of this course, learners will be able to:

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  • Understand AI/ML fundamentals and how they apply to clinical research

  • Work with clinical datasets for modeling and insights

  • Apply ML models to real-world problems in clinical trials and safety

  • Use Python to build and test basic AI/ML models

  • Interpret AI results in compliance with regulatory and ethical standards

Turn Clinical Data into Trusted Evidence with ADaM

  • Regulatory Submission Standard
    ADaM is required by global regulators (FDA, EMA, PMDA) for submitting statistical analysis datasets. Without ADaM-compliant datasets, a clinical trial cannot proceed to review or approval.

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  • Foundation for Statistical Analysis
    ADaM datasets are analysis-ready, traceable, and structured for efficient generation of TLFs (Tables, Listings, Figures). They reduce errors, ensure consistency, and speed up statistical programming.

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  • Critical Skill for Biostatistics & Programming Roles
    Proficiency in ADaM is a must-have for clinical SAS programmers, biostatisticians, and regulatory data specialists. It shows you're qualified to support advanced statistical reporting.

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  • Career Differentiator in CDISC Workflow
    Understanding ADaM makes you a complete CDISC professional, complementing your knowledge of SDTM, Define.XML, and submission readiness. It enhances your value in CROs, pharma, and global trials.

Course Contents

Module 1: Introduction to AI, ML & Clinical Research

  • What is AI & ML?

  • AI in life sciences: key trends and applications

  • Digital transformation in clinical development

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Module 2: Clinical Research Data Landscape

  • Types of data: EDC, EHR, ePRO, real-world data (RWD)

  • Structured vs unstructured data in clinical trials

  • Data cleaning and preprocessing basics

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Module 3: Machine Learning Fundamentals

  • Supervised vs Unsupervised learning

  • Common algorithms: Regression, Classification, Clustering, Decision Trees

  • Model evaluation metrics: accuracy, precision, recall, ROC

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Module 4: AI Use Cases in Clinical Trials

  • Patient recruitment optimization

  • AI for site selection and feasibility

  • Predictive models for dropout, adherence, and safety

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Module 5: Data Science in Clinical Data Management

  • Anomaly detection in data (fraud, error spotting)

  • Automating query generation and data cleaning

  • Data visualization and dashboarding

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Module 6: Pharmacovigilance and NLP Applications

  • AI in adverse event detection and signal detection

  • Natural Language Processing (NLP) for case narratives and safety data

  • Automating medical coding and narrative generation

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Module 7: Real-World Evidence & Regulatory AI

  • Use of AI/ML in observational studies and RWE generation

  • FDA/EMA perspectives on AI in clinical trials​

  • Ethical, legal, and compliance considerations (e.g., bias, explainability)

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Module 8: Hands-on AI/ML Projects

  • Python-based ML workflows using Jupyter Notebook

  • Build a model for patient classification or AE prediction

  • Present results and interpretations

Fact

Growing Demand for AI-Skilled Professionals

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CROs and pharma companies are actively hiring professionals who can bridge AI/ML with biostatistics, data management, and clinical operations.

Learn AI. Apply to Life Sciences. Drive the Future of Trials.

Empowering Clinical Research with the Intelligence of Machines

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