Description
Course Format
Key learning Objectives
Who should enroll
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Master Artificial Intelligence in Drug Safety. Shape the Future of Pharmacovigilance.
Master the future of drug safety by learning how Artificial Intelligence is transforming pharmacovigilance operations across the global pharmaceutical industry.
Gain practical, job-ready skills through real-world case studies, AI simulations, workflow exercises, and hands-on learning designed for modern pharmacovigilance professionals.
Artificial Intelligence is revolutionizing pharmacovigilance by enabling pharmaceutical and biopharmaceutical companies to automate adverse event processing, improve signal detection, enhance literature surveillance, optimize case management, and strengthen regulatory compliance. Consequently, professionals with expertise in both drug safety and AI are becoming increasingly valuable across the global life sciences industry. Royed Training’s AI in Pharmacovigilance Training is a comprehensive online certification program designed to help learners understand, implement, and manage AI-driven pharmacovigilance workflows using technologies such as Machine Learning, Natural Language Processing (NLP), Large Language Models (LLMs), AI Agents, Intelligent Document Processing, and Predictive Analytics.
Furthermore, this AI in Pharmacovigilance Course goes beyond theoretical concepts by providing practical exposure through simulation-based learning, real pharmaceutical case studies, workflow demonstrations, prompt engineering exercises, and industry-oriented assignments. Whether you are a pharmacovigilance professional, regulatory affairs specialist, clinical research professional, pharmacy graduate, or life science student, this certification equips you with the knowledge and practical skills needed to thrive in AI-enabled drug safety organizations. By the end of the program, participants will be prepared to contribute confidently to the digital transformation of pharmacovigilance while ensuring patient safety, regulatory compliance, and operational excellence.
No Prior Coding or AI Experience Required
AI PV Course Snapshots:
- Course Code: RYD-148
- Title of the course: Advanced Certification in AI in Pharmacovigilance (ACAiPv)
- Duration: 1 Month.
- Nature of the course: Online learning course. So the course can be accessed online anywhere 24×7.
- Eligibility : Graduation in any discipline. Even final year graduation students can apply.
- Course Certificate: Certificate will be provided at the end of the successful completion of the course.
Whether you’re involved in drug safety, labelling or pharmacovigilance process — this course empowers you to drive efficiency, accuracy, and innovation in your pharmacovigilance processes.
Course Format & Structure:
Elearning Lectures. Each Lecture Includes:
- Video Lectures
- Real-World Simulation
- Case Study
- Self Assessment Tests
- Downloadable Handouts
Key Learning Objectives
Upon successful completion of the AI in Pharmacovigilance Training, participants will be able to:
- Understand the fundamentals of Artificial Intelligence and its applications in modern pharmacovigilance.
- Explore how AI is transforming the complete pharmacovigilance lifecycle, from adverse event intake to regulatory reporting.
- Learn the practical use of Machine Learning, Deep Learning, Natural Language Processing (NLP), Large Language Models (LLMs), AI Agents, OCR, and Predictive Analytics in drug safety.
- Design AI-assisted workflows for adverse event collection, case processing, medical narrative analysis, literature screening, and signal detection.
- Apply AI technologies to automate repetitive pharmacovigilance activities while improving efficiency, quality, and compliance.
- Develop prompt engineering skills for extracting, summarizing, and analyzing pharmacovigilance data using Generative AI.
- Understand the implementation of Retrieval-Augmented Generation (RAG) and AI knowledge management in pharmacovigilance.
- Learn AI-assisted MedDRA coding, duplicate detection, case prioritization, and intelligent triage concepts.
- Interpret AI-generated outputs and perform effective Human-in-the-Loop (HITL) review and validation.
- Understand AI governance, regulatory expectations, ethical AI principles, data privacy, and validation requirements for pharmacovigilance systems.
- Evaluate AI implementation opportunities and digital transformation strategies within pharmaceutical and biopharmaceutical organizations.
- Gain practical experience through real-world case studies, workflow simulations, AI demonstrations, and industry-based assignments.
- Build future-ready skills to support AI-enabled pharmacovigilance operations and accelerate career growth in the pharmaceutical industry.
Who Should Enroll?
This AI in Pharmacovigilance Training is suitable for professionals and students who wish to build future-ready skills in AI-enabled drug safety.
The program is ideal for:
- Pharmacovigilance Professionals
- Drug Safety Associates
- Drug Safety Scientists
- Medical Reviewers
- Pharmacovigilance Case Processing Executives
- Pharmacovigilance Managers
- Signal Detection Specialists
- Medical Information Professionals
- Clinical Research Associates (CRA)
- Clinical Research Coordinators
- Regulatory Affairs Professionals
- Medical Affairs Professionals
- Quality Assurance Professionals
- Pharmaceutical IT Professionals
- Data Management Professionals
- Biotechnology Professionals
- CRO Professionals
- Pharmaceutical Consultants
- AI and Digital Transformation Professionals
- Pharmacy Graduates (B.Pharm, M.Pharm, Pharm.D)
- Life Science Graduates
- Biotechnology Graduates
- Healthcare Professionals interested in Artificial Intelligence
Royed Training’s AI in Pharmacovigilance Training offers a highly practical and industry-focused learning experience.
Practical Learning
- Simulation-based learning methodology
- Real pharmaceutical case studies
- Industry-oriented workflow demonstrations
- Practical AI implementation examples
- Scenario-based decision-making exercises
Artificial Intelligence Coverage
- Machine Learning
- Deep Learning
- Natural Language Processing (NLP)
- Large Language Models (LLMs)
- Retrieval-Augmented Generation (RAG)
- Intelligent Document Processing (IDP)
- Optical Character Recognition (OCR)
- AI Agents
- Predictive Analytics
- Explainable AI
- Responsible AI
- Human-in-the-Loop AI
Hands-on Exercises
- AI prompt engineering
- Safety narrative extraction
- AI-assisted adverse event processing
- Intelligent literature screening
- Signal detection workflows
- AI validation exercises
- AI implementation planning
- Digital pharmacovigilance transformation projects
Industry-Relevant Learning
- Global pharmacovigilance workflows
- Regulatory compliance considerations
- AI governance frameworks
- Inspection readiness concepts
- Drug safety quality management
- AI risk management strategies
Section 1Foundation of AI in Pharmacovigilance
Lecture 1Introduction to AI
Lecture 2Basics of AI ML DL | Key Differences | Application in industry
Lecture 3Types of AI
Lecture 4AI Fundamental Session 1
Lecture 5Comprehensive Training on AI Project | Managing Stages | AI Iteration
Lecture 6How machine learning
Lecture 7AI Application across drug life cycle | Case Analysis | Model predictions
Lecture 8AI in Pharmacovigilance - Overview and Evolution
Lecture 9Digital Pharmacovigilance Blueprint
Lecture 10The Augmented PV Professional - Moving from Manual Processing to Strategic Safety Intelligence in the AI Era
Lecture 11AI in the PV Lifecycle
Lecture 12AI Maturity in Drug Safety
Lecture 13PV Intelligent Ecosystem - Strategic Blueprint for AI Maturity in Pharmacovigilance
Lecture 14AI PV Ecosystem Transformation
Lecture 15Case Study : AI Drug Safety Transformation Impact
Lecture 16Scaling to Intelligent PV
Lecture 17Review Test and Case Simulations
Lecture 18Introduction to ML in Pharmacovigilance and Drug Safety Management
Lecture 19Deep Drive into ML Workflow - Use Case
Lecture 20ML Engine in PV - Strategic Impact
Lecture 21NLP - Chaos to Clarity | NLP Workflow | Real World Case Study
Lecture 22The Signal in the Noise | How NLP in transforming PV
Lecture 23Extracting Signal From Noise Advanced Session
Lecture 24NLP in PV - At a Glance
Lecture 25Case Analysis
Lecture 26Review Test and Simulation
Lecture 27 Introduction to LLMs in Drug Safety
Lecture 28Workflow and Operation of LLMs in PV and Drug Safety
Lecture 29Review Test
Lecture 30The Proactive Shift : Predictive Analytics in PV
Lecture 31RAG for PV and Safety Detection
Lecture 32AI Agent in PV
Lecture 33AI Agent in Safety Assessment
Lecture 34AI Agent Workflow - PV Use Case
Lecture 35Intelligent AI PV Ecosystem
Lecture 36How to Select Right AI Tool in PV
Lecture 37AI tools Selection Guide at a glance
Lecture 38Case Study Safety Detection in Mass Immunization Campaign
Section 2Data Science | AI automation in Pharmacovigilance and Regulatory Science
Lecture 39NLP | Conceptual understanding | Usage | NLP in Labeling
Lecture 40Data Grouping with Unsupervised Learning | Case Simulation | Hands on Exercise
Lecture 41Quality Data and Bias | Bias in datasets | Different Types of Bias in Pharma Datasets
Lecture 42AI Data Quality Standard | Checklist
Lecture 43Download AI Data Quality Checklist
Lecture 44Case Study: Accelerated Approval Using Real-World Evidence AI
Lecture 45AI Model for Dossier Submission and Filing - Model Selection | Simulation
Lecture 46Structured Vs. Unstructured Data | Practical Understanding | Explore Patient Datasets
Lecture 47Sample Data Exercise | Data error | Data Refinement and Cleaning of the data
Lecture 48Regulatory Affairs V4.0 | Complete Know How
Lecture 49Precision Calibration for Regulatory Ai | Framework | Use Case Identifications
Lecture 50Regulatory AI Tech Stack - Explainer
Lecture 51Advanced Guide to Regulatory AI Maturity
Lecture 52Strategic Mapping : AI in Regulatory Affairs
Lecture 53Architecting HITL - The Governance Framework for AI in Regulatory Affairs
Lecture 54Governing AI in Regulatory Affairs : A Classification Framework
Lecture 55NLP Based Regulatory Document Classification System
Lecture 56The Digital Thread | Document Governance
Lecture 57Concept of Data Lineage and Explainable AI - Explainer
Lecture 58Data Lineage and Explainable AI Model | Black Box Model | Glass Box Model | 8 Stage Continuous Execution Flow
Lecture 59AI Data Governance in RA | Privacy | Confidential | Data Deidentification | AI Privacy Framework
Lecture 60Mastering NER | How NER Workflow Works in AI RA | Case Based Explainer
Lecture 61Semantic Search in RA 4.0
Lecture 62Semantic Search in Regulatory Affairs and CMC Authoring
Lecture 63RAG Foundation on the context of RA 4.0 | End to end of Workflow
Lecture 64Regulatory RAG Blueprint
Lecture 65Meet Your Digital Co-Worker | Introduction to RPA
Lecture 66Regulatory RPA Blueprint | Framework | 8 Steps Playbook for operational Deployment
Lecture 67RPA - At Glance
Lecture 68Architecting the Digital Regulatory Workforce | AI | RPA | Agentic Automation
Lecture 69Regulatory Affairs Automation
Lecture 70Decoding the Automation Landscape
Section 3Prompt Engineering
Lecture 71Prompt Engineering – Basic Foundations of Prompt Writing | Different Prompt Framework | Case Based Examples
Lecture 72Special Prompting Structure - Case Based Prompt Designing
Lecture 73Special Prompting Structure - Case Based Prompt Designing
Lecture 74PE04 Workflow for writing AI Prompts with Life Science Industry Use Cases
Lecture 75PE06 PE Parameters | Controlling Creativity, Accuracy, and Output Behavior for Life-Science Applications | Simulation based decision making
Lecture 76How to adjust the parameters | Techniques
Lecture 77Creating A Detailed Regulatory Prompt | Designing Structured Prompt in Code Editor
Section 4Risk and Compliance Management
Lecture 78Data Integrity issues in Pharmaceutical Industry : Detailed Understanding
Lecture 79Establishment Inspection Report | 483 Observations | USFDA Warning Letter | Handling of FDA inspection | Closing of Warning Letter
Lecture 80Electronic Batch Record for effective compliance management | Key Understanding | Functionalities | Importance in managing data integrity
Lecture 81RTQs | Response to Queries | How to handle Regulatory Queries
Lecture 82AI and Data Integrity | Regulatory Documentation | Case Based Learning | Understanding Data Integrity Principles | Application and Use Cases
Lecture 83Cases of data breaches with explanation
Lecture 84Case Based Discussion : Data Breach in AI-Assisted CMC Drafting
Section 5AI in Labelling and Artwork
Lecture 85CCDS Management
Lecture 86CCDS Management Tools - Native, AI Plus | Key Functionalities
Lecture 87AI Augmented Labelling Compliance System
Lecture 88Label Drift in CCDS Management | Simulation Case Study | AI Based Workflow in Label Drift detection and management
Lecture 89AI Review of Labels, PI and SMPC with help of the Case Based Simulation | 4 Case Simulations
Lecture 90CCDS Management Case Study | Pregnancy Warning Upgrade Case Study
Lecture 91CCDS Management Case Study | Dosage Section Conflict | Case Based Analysis
Lecture 92Centralized Artwork Operation | Mechanism | Operation Step Planning
Lecture 93Case Study on Centralized Artwork Operation [Ref: Recall Management]
Lecture 94Artwork Management Terms | Key Understanding on Terminologies | Usage in Artwork Cases
Lecture 95Source Documents | Artwork Brief
Lecture 96Creating and Reviewing Label Content | Stakeholders of Labelling and Artwork Management | Case Studies
Lecture 97Change control process for labels | Types of changes | Impact Assessment | Concept of Greyzone Changes
Lecture 98Label Impact Assessment Checklist
Lecture 99Packaging Types & Their Impact on Artwork
Lecture 100Design tool used in artwork | Esko Platform | Adobe Illustrator | CorelDRAW | File Format | Label Design Workflow and Tool Utilization
Lecture 101Labelling Management System (LMS) | Understanding of Popular LMS Softwares | Workflow Management | Workflow Automation & Version Tracking | Best Practices
Lecture 102Concept of eLabel (Electronic Labeling) in Pharmaceuticals
Section 6Real World Evidence and Real World Data | Strategic Decision Making
Lecture 103Evidence Based Decision Making | Combine evidences for decision making | Do we need more evidence?
Lecture 104RWD and RWE | Potential sources of RWE | Traditional RCTs vs. RWE | Case Study - RWE Programs | Influencing HCP decision-making
Lecture 105RWD and RWE in Product Lifecycle Management
Lecture 106RWD and RWE - Fit to use | Assessment
Lecture 107RWD data sources | Different types | Detailed understanding of each class
Lecture 108RWD Study Design
Lecture 109RWE Published Tool | Insights on commonly used tools
Lecture 110Healthcare Reimbursement Models : Value Based Care | Fee for Service (FFS) Model | Other Reimbursement Models
Lecture 111Consensus Narrative Review
Lecture 112Electronic Patient Data | EMR | EHR | Differences | Software architecture and characteristics
Lecture 113Active Surveillance Schemes | Active Case Finding |Sentinel Surveillance | Cohort Studies | Vaccine Safety Surveillance | Pharmacovigilance Programs | Disease Registries |Event Monitoring
Lecture 114RWD Characteristics
Lecture 115RCT vs. RWE Comparison | Case Based Analysis
Lecture 116RCT and RWE Comparison
Section 7Working on Healthcare Datasets
Lecture 117Introduction to Healthcare Datasets
Lecture 118Dataset 1: Claims Data (Insurance)
Lecture 119Dataset 2: Retail Pharmacy Prescription Data
Lecture 120Dataset 3: Longitudinal Patient Data
Lecture 121Characteristics of Different Set of Healthcare Data
Lecture 122Reimbursement and Pricing Datasets
Lecture 123RWE Data Sources & Quality Considerations Checklist | Case Based Analysis
Lecture 124Triangulation and benchmarking | Enhancing Data Validation Through Cross-Referencing
Lecture 125RWD & RWE Case Database
Lecture 126RWE Large Dataset for Data Crunching Exercises |Data cleaning & preprocessing | Comparative Effectiveness Analysis
Section 8AI Tools, Ethics and Future Landscape
Lecture 127AI Reg Tools - Review | Understanding Capabilities
Lecture 128Regulatory Authority View on AI Adoption
Lecture 129Data Sensitivity | Levels | Consequences | Management
Lecture 130Governance Flowchart – AI Tools in GxP Environments
Lecture 131AI Data Governance in RA | Privacy | Confidential | Data Deidentification | AI Privacy Framework
Lecture 132The Digital Thread | Document Governance
Lecture 133Secure AI Workflow Guide - Introductory Session
Lecture 134Secure AI Pipeline | The Mechanics of De-identification | De-identification vs. Anonymization
Section 9AI Tools
Lecture 135RA Example - Generalist Vs. Specialist AI Tools
Lecture 136RA Specific Examples - AI Generalist Vs. AI Specialist Tools
Lecture 137Horizontal vs Vertical AI Tools
Lecture 138AI Tool Assessment Test
Lecture 139Citation Verification Workflow | AI Tools Management | Hallucination and HITL Action Plan
Section 10AI in Regulatory Authoring and CMC Writing
Lecture 140AI-Powered Regulatory Document Authoring and CMC Writing
Lecture 141AI Tools Comparison - Authoring and CMC Writing (Module 2 & 3)
Lecture 142Tips for Selecting AI Tools (CMC Authoring)
Lecture 143CMC - AI Workflow Checklist | Simulation Based Cases
Lecture 144HITL Regulatory Document Authoring
Lecture 145Confidence Score in Regulatory Authoring | Case Simulation in CMC Authoring
Lecture 146Data Extract in Regulatory Writing [Case Simulation]
Lecture 147CMC AI Readiness Playbook | Repository Readiness | Metadata Standard | Eight Step Process
Lecture 148Selecting AI Tools for CMC Authoring
Lecture 149Pilot Testing AI in CMC
Lecture 150Custom CMC Authoring Tool
Lecture 151AI Confidence Scores
Lecture 152Practical Framework of CI Deployment
Lecture 153Confidence Score Golden Rules
Lecture 154AI Trust Bridge | Source to Output Traceability in CMC Authoring
Lecture 155AI Traceability Lifecycle | Case Simulation
Section 11eCTD Publishing and Advanced eCTD v4.0 Training
Lecture 156Fundamentals of eCTD
Lecture 157eCTD v4.0 Structure and Life Cycle
Lecture 158eCTD Validation | Managing Validation | Tool Types | Types of Error | Validation Error Management
Lecture 159eCTD Validation Tools
Lecture 160eCTD Publishing Cycle
Lecture 161HL7 - Structure and Understanding in the context of eCTD V4.0
Lecture 162Tips and Submission Readiness Checklist
Lecture 163eCTD Submission Checklist Format
Lecture 164Regulatory Publishing Document Control Challenges
Lecture 165Document Control Checklist
Lecture 166Leaf Structure Analysis | Common Error in Leaf Structuring
Lecture 167Formatting Rules Templated and Agency Specification
Lecture 168Versioning for Regulatory Publishing
Lecture 169File Naming Standard
Lecture 170Text Recognition and Optical Character Recognition (OCR)
Lecture 171Versioning File Naming OCR - How they work together
Lecture 172Final Document Assembly
Lecture 173Visual Diagram : eCTD Structure and XML Hierarchy
Lecture 174TOC and CI Management
Lecture 175Initial Submission & Maintenance Submission
Lecture 176Managing Lifecycle Operators - NEW REPLACE DELETE APPEND
Lecture 177eCTD Dossier Lifecycle - Multi Sequence View
Lecture 17810 Year eCTD Lifecycle : Sequence Evolution Diagram
Lecture 179Global Decision Tree - Classifying Post Approval Changes
Lecture 180eCTD Technical Validation | Typical issues | Resolutions
Lecture 181eCTD Validation Readiness Checklist
Lecture 182QC Checklist Design and Pre Submission Review
Lecture 183Submission Gateway | FDA ESG • EMA CESP • Health Canada Portal • PMDA Gateway
Lecture 184Regulatory Publishing Simulation
Lecture 185Pre-Gateway Submission Review Checklist | Post Submission Confirmation
Section 12Request for course certificate
Lecture 186Final Examination
Lecture 187Request for Course Certificate
