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Certificate Program

Artificial Intelligence & Machine Learning for Clinical Research and Data Management

Program Objective

This program is designed to equip learners with practical knowledge and hands-on exposure to Artificial Intelligence (AI) and Machine Learning (ML) applications in Clinical Research and Clinical Data Management (CDM). The course bridges the gap between traditional clinical research processes and modern data-driven approaches, enabling participants to understand how AI/ML improves data quality, operational efficiency, risk-based monitoring, and regulatory compliance across clinical trials.

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

Project governed by: IDDCR Global Research CRO  

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Designed For

  • Clinical Research Professionals

  • Clinical Data Management (CDM) Professionals

  • Clinical Programmers / SAS Programmers

  • Biostatisticians

  • Pharmacovigilance Professionals

  • Life Sciences, Pharmacy, Biotechnology Graduates & Postgraduates

  • Data Analysts aspiring to enter Healthcare / Clinical Research

  • Medical Graduates interested in Research & Data Science

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Pre-requisite

  • Basic understanding of Clinical Research or Healthcare domain

  • Fundamental knowledge of statistics is preferred

  • No prior AI/ML coding experience required (basics covered in the program)

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Learning Delivery

  • Live instructor-led online sessions

  • Conceptual lectures + real-world use cases

  • Tool demonstrations

  • Hands-on exercises using healthcare-relevant datasets

  • Interactive discussions and Q&A

  • Case-study-based learning from clinical trial scenarios

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Tools & Technologies Used

  • ChatGPT (Clinical Research–specific prompting)

  • Generative AI tools for documentation, analysis, and automation

  • AI-powered data review & quality tools (conceptual + applied)

  • No-code / low-code ML tools (AutoML-style usage)

  • iClinicalAI – IDDCR’s proprietary Agentic AI platform for Clinical Research & CDM

  • Prompt libraries developed by IDDCR Global Research CRO.

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Assessment & Certification

  • Prompt-based assignments

  • Tool-driven mini case studies

  • Agent creation & workflow execution assessment

  • Final practical evaluation using iClinicalAI

  • Certificate upon successful completion

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Learning Outcome

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

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  • Use Generative AI confidently in Clinical Research & CDM tasks

  • Apply ML-powered tools without coding for insights and decision support

  • Design and deploy Agentic AI workflows for clinical operations

  • Create and optimize clinical research–specific prompts

  • Work efficiently with AI while maintaining regulatory and data integrity

  • Position themselves as AI-enabled Clinical Research professionals

Stop learning theory. Start applying AI

  • “You don’t need to become a programmer to stay relevant in the AI era of Clinical Research — you need to become an AI-enabled professional.”

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  • “AI is already reshaping Clinical Research. The question is not if it will impact your role, but how prepared you are to work with it.”​​

Course Contents

Module 1: Generative AI Fundamentals for Clinical Research

  • Overview of Drug Discovery & Clinical Development

  • Phases of Clinical Trials (I–IV)

  • Key stakeholders in Clinical Research

  • Clinical Research data lifecycle

  • What is Generative AI (explained for non-technical users)

  • How GenAI is transforming Clinical Research & CDM

  • Understanding LLMs from a user perspective

  • Safe and compliant use of AI in regulated environments

  • Introduction to prompt-based workflows

  • Clinical Research–specific AI use cases

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Module 2: Generative AI in Clinical Documentation & CDM

  • AI-assisted protocol understanding & summarization

  • CRF review and consistency checks using GenAI

  • AI for data cleaning logic explanation (non-technical)

  • Query drafting & query response suggestions

  • Medical writing support using GenAI

  • Hands-on: Prompting for CDM & CR activities

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Module 3: Advanced Prompt Engineering for Clinical Research

  • Prompt frameworks for Clinical Research

  • Designing prompts for: SDTM & ADaM understanding (conceptual), Edit check logic explanation, Data review narratives, Audit & inspection readiness

  • Building reusable prompt libraries

  • Hands-on: Creating role-based prompts (CDM, CRA, Medical Writer)

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Module 4: Applied Machine Learning Tools

  • What ML really means for clinical professionals

  • Difference between AI, ML, and GenAI (practical view)

  • ML-powered tools used in: Risk-Based Monitoring (RBM), Site performance analysis, Data anomaly detection

  • Using ML dashboards & AutoML-style tools (no coding)

  • Interpreting ML outputs for decision-making

  • Case study: ML-driven clinical risk assessment

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Module 5: Agentic AI for Clinical Research & CDM

  • What is Agentic AI (simple, workflow-based explanation)

  • Difference between GenAI vs Agentic AI

  • Introduction to iClinicalAI platform

  • Understanding agents, tasks, memory, and workflows

  • Pre-built IDDCR agents for: CDM review, Query management, Clinical documentation

  • Live demo: End-to-end AI-driven CDM workflow

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Module 6: Practical Use Cases, Career Pathways & Capstone

  • Hands-on mini project using clinical-like datasets

  • Designing AI agents without coding

  • Defining agent roles (CDM Agent, CRA Agent, QA Agent)

  • Writing prompts for autonomous execution

  • Connecting multiple agents in a workflow

  • Interpretation of ML outputs for clinical decision-making

  • Role mapping: Clinical Data Scientist, AI-enabled CDM professional, Clinical Research Analyst

  • Career guidance and industry expectations

  • Final assessment & feedback session​

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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