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EU GMP Annex 22 : Artificial Intelligence Compliance in Pharma Course & Certification
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EU GMP Annex 22 : Artificial Intelligence Compliance in Pharma
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About
The EU GMP Annex 22: Artificial Intelligence Compliance in Pharma Course & Certification is designed to provide a practical understanding of regulatory expectations for the use of Artificial Intelligence (AI) in pharmaceutical manufacturing, quality systems, and GxP environments. The course focuses on ensuring AI systems are developed, validated, and used in a compliant, transparent, and risk-controlled manner.
This course covers EU GMP Annex 22 principles, AI lifecycle management, model validation, data integrity, algorithm transparency, risk-based approach, and regulatory expectations for AI-driven systems. It also emphasizes audit readiness, documentation requirements, governance frameworks, and best practices for implementing compliant AI solutions in pharma operations. Upon successful completion, learners receive a certification demonstrating competency in AI compliance under EU GMP Annex 22.
Who Should Enrol?
- Quality Assurance and Compliance Professionals
- Validation and CSV Professionals
- Pharmaceutical Manufacturing and IT Teams
- Regulatory Affairs Professionals
- Data Science and AI/ML Professionals in Life Sciences
- Clinical Operations and Data Management Teams
- Pharmaceutical and Biotechnology Employees
- Life Science, Pharmacy, Chemistry, Engineering, and IT Graduates
What you will learn
Understand the fundamentals of AI in GxP environments, including its role, scope, and regulatory expectations under EU GMP Annex 22.
Learn how a risk-based approach applies to AI systems, including intended use definition, model governance, data quality, and development controls.
Gain knowledge of AI validation principles such as performance testing, explainability, transparency, human oversight, and lifecycle management.
Develop an understanding of continuous monitoring, audit readiness, and real-world case studies to ensure compliant and reliable AI deployment in pharmaceutical environments.
Course Syllabus
- What Is Artificial Intelligence in GxP Context?
- The Spectrum of AI in Pharma and MedTech
- Why Traditional CSV Falls Short for AI
- AI Lifecycle - End-to-End
- Why AI Skills Are Now Core QA Capability
- Why AI in GxP Is High-Stakes
- Operating Principles for Trustworthy AI
- Stakeholders and Responsibilities
- QA's Role in AI Governance
- Validation Team's Role in AI
- IT and Data Science's Role in AI Governance
- Senior Leadership's Role in AI Governance
- AI Vendor Governance Considerations
- Common Pitfalls in Early AI Adoption
- What Annex 22 Is and Why It Matters
- Origin and Drafting Context
- Annex 22 Diagram - Position in the Framework
- Annex 11 vs Annex 22 - Working Together
- Annex 22 - Foundational Principles
- Scope - What Annex 22 Covers
- Scope - What Annex 22 Excludes
- Annex 22 Scope - Static vs Dynamic AI
- Static AI - The Default Position
- Dynamic AI - Restricted Use
- Generative AI Under Annex 22
- Annex 22 Section Structure - At a Glance
- Common Misinterpretations of Annex 22
- Why Scope Matters First
- First Question - Is It AI Under Annex 22?
- Second Question - Does It Affect GMP?
- AI Risk-Based Scoping
- Static vs Dynamic AI
- Static vs Dynamic Classification
- Generative AI in GMP - Specific Considerations
- Generative AI Scope Considerations
- Scoping Decision Process
- AI System Inventory - The Foundational Tool
- Boundary Cases - When Scoping Is Hard
- Industry Examples of Scoping
- Inspector Perspective on Scope
- Industry Trends in AI Scope Boundaries
- Documenting Scope Decisions Defensibly
- Why Risk-Based Matters
- ICH Q9 Foundations Apply to AI
- AI-Specific Failure Modes for Risk Assessment
- Patient Impact - The Primary Risk Dimension
- Risk-Based Validation Effort
- Process Criticality Considerations
- Decision Reversibility
- Detection - Can the Failure Be Caught?
- Risk Acceptance Decisions
- Likelihood Assessment for AI
- Severity Considerations Beyond Patient Impact
- Inspector Perspective on AI Risk
- Risk Assessment in AI Procurement
- Why Intended Use Is Foundational
- What Intended Use Means Under Annex 22
- Components of an Intended Use Specification
- AI Risk-Based Validation Effort
- Intended Use - Out-of-Scope Conditions
- Annex 11 vs Annex 22
- Model Definition Specifications
- Linking Intended Use to Validation
- Intended Use Patterns - Worked Examples
- Worked Example - Visual Inspection AI Intended Use
- Communicating Intended Use to Operators
- Worked Example - Deviation Triage AI Intended Use
- Worked Example - Process Control AI Intended Use
- Common Intended Use Pitfalls
- Intended Use Approval Process
- Operational Discipline - Staying Within Intended Use
- Intended Use Versioning Strategy
- Inspector Perspective on Intended Use
- Why Data Governance Drives AI Quality
- Data Governance Scope Under Annex 22
- AI Data Pipeline Quality Gates
- ALCOA+ Applied to AI Data
- ALCOA+ Extensions for AI Data
- Training Data Lineage
- Training Data Quality Review
- Training Data Approval and Versioning
- Data Lineage Tooling and Practice
- Training Data Bias Considerations
- Test Data Independence
- Test Data Composition Requirements
- Operational Data Governance
- Data Source Validation
- Labelling and Annotation Governance
- Synthetic Data Considerations
- Data Privacy and Security Considerations
- Data Governance in Practice
- Operational Data Capture Architecture
- Inspector Perspective on Data Governance
- Data Governance Tooling Considerations
- Why Development Discipline Matters
- From Data Science to Engineering Discipline
- Reproducibility as a Foundational Requirement
- Version Control - Code, Data, and Models
- AI Data Pipeline Quality Gates
- MLOps Platform Considerations
- Algorithm Selection Methodology
- Hyperparameter Tuning Discipline
- Bias Prevention During Development
- Training Pipeline Architecture
- Reproducibility Tooling Examples
- Training Documentation Requirements
- Model Card Documentation
- Reproducibility Verification
- Development Practices in Detail
- Cross-Validation Methodology
- Hyperparameter Search Documentation
- Adversarial Testing During Development
- Calibration Verification
- Continuous Integration for ML
- Development Governance Records
- Inspector Perspective on Development
- Why AI Validation Is Different
- Conventional CSV vs AI Validation
- AI Validation Lifecycle
- AI Lifecycle - End-to-End
- Validation Plan Components
- Data Qualification - Training Data
- Data Qualification - Test Data
- Model Validation Metrics Quad
- Acceptance Criteria Definition
- Boundary Case Testing
- Operational Performance Qualification
- Validation Documentation
- Validation Report Structure
- Evidence Traceability
- Re-Validation Triggers
- Validation Failures and Lessons
- Inspector Perspective on Validation
- Validation Cost Considerations
- Why Explainability Matters
- Explainability Definitions
- Explainability Spectrum
- White-Box Models
- Grey-Box Models
- Black-Box Models
- SHAP - Shapley Additive Explanations
- LIME - Local Interpretable Model-Agnostic Explanations
- Explanation Audiences and Formats
- Surrogate Models
- Attention Visualisation
- Explainability Proportionate to Criticality
- Documenting Explanations
- Transparency Beyond Model Interpretability
- Practical Explainability Implementation
- Counterfactual Explanations
- Real Explainability Challenges
- Inspector Perspective on Explainability
- Why Human Oversight Matters
- Human Oversight Tiers
- Designing Meaningful Oversight
- Avoiding Automation Bias
- Oversight Design Patterns
- Reviewer Selection and Qualification
- Oversight Documentation Requirements
- Reviewer Training Programmes
- Override Authority and Procedures
- Oversight in Operation
- Engagement Metrics
- Reviewer Calibration
- Oversight Quality Audits
- Oversight Workflow Implementation
- Escalation Mechanisms
- Oversight Failures and Lessons
- Inspector Perspective on Oversight
- Module 10 Summary - Human Oversight
- Oversight Cost-Benefit Analysis
- Cross-Functional Oversight Roles
- Why Lifecycle Discipline Matters
- Lifecycle Phases Under Annex 22
- Continuous Performance Monitoring
- Three Types of Drift
- Data Drift Detection
- Concept Drift Detection
- Performance Drift Detection
- Drift Investigation and Response
- Drift Threshold Selection
- Out-of-Distribution Detection in Operation
- Change Control for AI Systems
- Retraining Strategy
- Configuration Management
- Lifecycle Integration with Quality System
- AI System Retirement
- Lifecycle Failures and Lessons
- Inspector Perspective on Lifecycle
- Why Daily Discipline Matters
- What Inspectors Look For
- Inspection Findings Severity
- AI Inventory as Foundation
- Documentation Hierarchy
- Validation Evidence Organisation
- Inspection Response Practice
- Inspection Response Workflow
- During-Inspection Conduct
- Common Inspection Questions
- Pre-Inspection Preparation Checklist
- Defending Specific Predictions
- Findings Response and CAPA
- Building Inspection-Ready Programmes
- Internal Audits of AI Programmes
- Mock External Inspections
- Cross-Functional Fluency Development
- Document Currency Disciplines
- Inspection Coordination
- Post-Inspection Improvement Cycle
- Lessons from External AI Inspections
- Inspection Documentation Hierarchy
- Why Case Studies Matter
- How to Use This Module
- Manufacturing AI Failures
- Laboratory AI Failures
- Supply Chain and Pharmacovigilance
- Cross-Cutting Lessons
Course Benefits
CPD Points
Gain Continuing Professional Development (CPD) Points, accredited by The Faculty of Pharmaceutical Medicine of the Royal College of Physicians of the United Kingdom. These can be used to count towards the distance learning element of any scheme that comes under the umbrella of The Academy of Medical Royal Colleges or any other scheme for which there is mutual recognition.
Certification
Receive a personal certificate to show your subject knowledge on course completion.
Affordable
You get excellent value through our cost-effective prices. We can also offer you group discounts on larger purchases.
Flexibility
The course saves you time through the convenience of online availability. This lets you complete the interactive course at your own comfort.
Keep Up to Date
You will stay up to date with any changes to EU GMP Annex 22, the EU AI Act, FDA Predetermined Change Control Plans, and ICH AI principles as our training courses are constantly monitored, reviewed and updated.
Learn from Industry Experts
The course content has been developed by industry practitioners and subject-matter experts to ensure that learners can apply the principles directly in their daily work. Every module follows a consistent rhythm — concept, regulatory context, real-world failure, audit perspective, preventive controls, and a knowledge check.
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