InstructorRoyed Training
TypeOnline Course
Price$350 / 29500 INR.
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Artificial Intelligence AI in Pharma

Introduction

Who should attend

What you will learn

Related Courses

Artificial Intelligence AI in Pharma Training Course

Royed Training’s Advanced Certification on Artificial Intelligence in Pharma is a specialized online training program crafted to empower life science professionals with the essential knowledge and hands-on skills to understand and apply AI across pharmaceutical and healthcare domains.

This impactful course delivers fundamental AI concepts in a practical and accessible format, making it ideal for freshers and also for working professionals who want to upskill without disrupting their busy schedules. With a flexible learning approach, you can progress at your own pace while gaining industry-relevant expertise.

Through concise e-learning lectures, interactive simulations, quizzes, and real-world case studies, learners will explore how AI is transforming drug discovery, clinical development, pharmacovigilance, regulatory affairs, medical affairs, real-world evidence, and pharma marketing. This course also introduces key concepts such as machine learning, deep learning, data quality standards, and ethical considerations for AI adoption in healthcare.

By the end of this course, participants will be equipped with foundational AI literacy and practical insights to identify opportunities for AI integration within their functions—positioning themselves for future-ready roles in a data-driven pharmaceutical ecosystem.

Snapshot of the course

  • Course Code: RYD-134
  • Course Title: Advanced Certification Course on Artificial Intelligence (AI) in Pharma
  • Access Duration: 1 Month
  • Nature of the course: Online distance learning course. Course can be accessed online across anywhere 24×7.
  • Certification: Certificate will be provided at the end of the successful completion of the course.

Who should attend?

  • Pharma and biotech professionals in R&D, regulatory, PV, marketing, and medical affairs
  • Clinical researchers and pharmacovigilance specialists
  • Market access and health economics professionals
  • Pharma educators and consultants looking to add AI to their skillset

What you will learn in Artificial Intelligence (AI) in Pharma Training?

By the end of the course, participants will be able to:

  • Foundations of Artificial Intelligence (AI): Understand core AI concepts, terminology, and the differences between AI, machine learning (ML), and deep learning (DL).
  • AI in Drug Discovery and Development: Learn how AI accelerates target identification, molecular screening, and clinical trial design.
  • AI Applications Across the Pharma Value Chain: Explore AI’s role in regulatory affairs, pharmacovigilance, medical writing, medical affairs, and market access.
  • AI in Real-World Evidence (RWE) & HEOR: Discover how AI enhances data extraction, patient segmentation, and real-world outcome analysis.
  • Natural Language Processing (NLP) in Pharma: Understand how NLP is used to analyze scientific literature, clinical notes, and social listening for safety and marketing insights.
  • Data Quality & Governance for AI: Grasp the importance of clean, structured data and regulatory compliance for successful AI adoption.
  • Ethical and Regulatory Considerations: Learn about challenges such as bias, transparency, explainability, and ethical AI use in healthcare.
  • Future Trends and Career Impact: Identify emerging AI trends and how they shape the future roles of life science professionals.

Note:

This first-of-its-kind, application-driven certification course delivers a unique and focused learning experience. It seamlessly combines foundational AI concepts with real-world pharmaceutical applications—designed specifically for life science professionals. Unlike generic AI programs, this course moves beyond theory. It offers practical, hands-on insights into how AI is transforming the pharma value chain.

From drug discovery and clinical trials to regulatory affairs, pharmacovigilance, and market strategy—every key area is covered through relevant use cases. The course integrates essential AI literacy with functional learning, making complex ideas easy to understand. As a result, participants gain not only core AI knowledge but also the ability to critically assess and apply AI tools in daily pharma operations.

Ultimately, this program equips professionals to stay future-ready and competitive in the rapidly evolving pharmaceutical and biotech landscape.

Related Courses

Section 1Fundamentals of Artificial Intelligence (AI)
Lecture 1Introduction to AI
Lecture 2Basics of AI ML DL | Key Differences | Application in industry
Lecture 3Types of AI
Lecture 4AI Project | Overview
Lecture 5Comprehensive Training on AI Project | Managing Stages | AI Iteration 
Lecture 6How machine learning
Section 2AI In R&D, Drug Discovery and Regulatory Functions
Lecture 7Understanding Regulatory Affairs – Traditional vs. Digital | 2 Case Study
Lecture 8Basics of AI for Regulatory Professionals | Understanding different models used in RA | Case Study | Simulation
Lecture 9AI Application across drug life cycle | Case Analysis | Model predictions
Lecture 10AI Terms with Regulatory Significance | Regulatory Applications
Lecture 11NLP in Regulatory Affairs | Conceptual understanding | Usage | NLP in Labeling
Lecture 12Case Study: Accelerated Approval Using Real-World Evidence AI
Lecture 13AI Model for Dossier Submission and Filing - Model Selection | Simulation
Lecture 14AI Technologies in RA
Section 3AI and Data Science
Lecture 15Data Grouping with Unsupervised Learning | Case Simulation | Hands on Exercise
Lecture 16Quality Data and Bias | Bias in datasets | Different Types of Bias in Pharma Datasets 
Lecture 17AI Data Quality Standard | Checklist
Lecture 18Download AI Data Quality Checklist
Lecture 19Structured Vs. Unstructured Data | Practical Understanding | Explore Patient Datasets
Lecture 20Sample Data Exercise | Data error | Data Refinement and Cleaning of the data 
Section 4AI in Regulatory and Medical Writing
Lecture 21AI-Powered Regulatory Document Authoring and CMC Writing
Lecture 22AI Tools Comparison - Authoring and CMC Writing (Module 2 & 3)
Lecture 23Tips for Selecting AI Tools (CMC Authoring)
Lecture 24CMC - AI Workflow Checklist | Simulation Based Cases
Lecture 25HITL Regulatory Document Authoring
Lecture 26Confidence Score in Regulatory Authoring | Case Simulation in CMC Authoring
Lecture 27Confidence Scores in Data Extract in Regulatory Writing [Case Simulation]
Section 5AI in Submission Management | eCTD Management
Lecture 28eCTDV4.0 Fundamental Understanding | Structures | Key Features
Lecture 29eCTDV4.0 Structure | Lifecycle Management 
Lecture 30eCTD Validation | Managing Validation | Tool Types | Types of Error | Validation Error Management
Lecture 31eCTD Validation Tools
Lecture 32eCTD Publishing Cycle
Lecture 33HL7 - Structure and Understanding in the context of eCTD V4.0
Lecture 34Tips and Submission Readiness Checklist
Lecture 35eCTD Submission Checklist Format
Section 6AI and Data Integrity | Impact and Action Plan in AI implementation
Lecture 36Data Integrity issues in Pharmaceutical Industry : Detailed Understanding 
Lecture 37Establishment Inspection Report | 483 Observations | USFDA Warning Letter | Handling of FDA inspection | Closing of Warning Letter 
Lecture 38Electronic Batch Record for effective compliance management | Key Understanding | Functionalities | Importance in managing data integrity
Lecture 39RTQs | Response to Queries | How to handle Regulatory Queries 
Lecture 40AI and Data Integrity | Regulatory Documentation | Case Based Learning | Understanding Data Integrity Principles | Application and Use Cases
Lecture 41Cases of data breaches with explanation
Lecture 42Case Based Discussion : Data Breach in AI-Assisted CMC Drafting 
Section 7AI in Artwork Management
Lecture 43CCDS Management 
Lecture 44CCDS Management Tools - Native, AI Plus | Key Functionalities
Lecture 45AI Augmented Labelling Compliance System
Lecture 46Label Drift in CCDS Management | Simulation Case Study | AI Based Workflow in Label Drift detection and management
Lecture 47AI Review of Labels, PI and SMPC with help of the Case Based Simulation | 4 Case Simulations 
Lecture 48CCDS Management Case Study | Pregnancy Warning Upgrade Case Study
Lecture 49CCDS Management Case Study | Dosage Section Conflict | Case Based Analysis
Lecture 50Centralized Artwork Operation | Mechanism | Operation Step Planning 
Lecture 51Case Study on Centralized Artwork Operation  [Recall Management]
Lecture 52Artwork Management Terms | Key Understanding on Terminologies | Usage in Artwork Cases
Section 8Real World Data and Real World Evidence
Lecture 53Evidence Based Decision Making | Combine evidences for decision making | Do we need more evidence? 
Lecture 54RWD and RWE | Potential sources of RWE | Traditional RCTs vs. RWE | Case Study - RWE Programs | Influencing HCP decision-making
Lecture 55RWD and RWE in Product Lifecycle Management
Lecture 56RWD and RWE - Fit to use | Assessment 
Lecture 57RWD data sources | Different types | Detailed understanding of each class 
Lecture 58RWD Study Design
Lecture 59RWE Published Tool | Insights on commonly used tools
Lecture 60Healthcare Reimbursement Models : Value Based Care | Fee for Service (FFS) Model | Other Reimbursement Models
Lecture 61Consensus Narrative Review 
Lecture 62Electronic Patient Data | EMR | EHR | Differences | Software architecture and characteristics
Lecture 63Active Surveillance Schemes | Active Case Finding |Sentinel Surveillance | Cohort Studies | Vaccine Safety Surveillance | Pharmacovigilance Programs | Disease Registries |Event Monitoring
Lecture 64RWD Characteristics
Lecture 65RCT vs. RWE Comparison | Case Based Analysis
Lecture 66RCT and RWE Comparison
Section 9Working on Healthcare Datasets
Lecture 67Introduction to Healthcare Datasets
Lecture 68Dataset 1: Claims Data (Insurance)
Lecture 69Dataset 2: Retail Pharmacy Prescription Data
Lecture 70Dataset 3: Longitudinal Patient Data
Lecture 71Characteristics of Different Set of Healthcare Data
Lecture 72Reimbursement and Pricing Datasets
Lecture 73RWE Data Sources & Quality Considerations Checklist | Case Based Analysis
Lecture 74Triangulation and benchmarking | Enhancing Data Validation Through Cross-Referencing
Lecture 75RWD & RWE Case Database
Lecture 76RWE Large Dataset for Data Crunching Exercises |Data cleaning & preprocessing | Comparative Effectiveness Analysis 
Section 10AI Tools, Ethics and Future Landscape
Lecture 77AI Reg Tools - Review | Understanding Capabilities
Lecture 78Regulatory Authority View on AI Adoption
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