Pre-Conference Course on Basics of AI
The pre-conference is aimed at
- Managers and interested parties from the pharmaceutical industry
- Suppliers
- Service companies
2nd Annex 22/ AI Conference 2025
The conference is aimed
- At managers and interested parties from the pharmaceutical industry
- Suppliers
- Service companies
Pre-Conference Course on Basics of AI
What can you expect in the Pre-Conference Course?
- Get to know the basics of AI (Artificial Intelligence) and ML (Machine Learning),
- Learn about the importance of data and models,
- Understand the difference between specialised AI and generative AI,
- Risk management and validation; how should these classic pharmaceutical topics be considered and applied in the context of AI and
- What are the applications and limitations in the GxP environment? Learn about the current state of development.
2nd Annex 22 / AI Conference
- You will gain an overview of the current state of regulatory development with regard to the use of AI in the pharmaceutical industry
- You will be able to better assess the possibilities and limitations of this technology
- You will learn more about the requirements for successfully implementing AI projects within the company
- Case studies from pharmaceutical companies will show you possible areas of application for AI
Pre-Conference Course on Basics of AI - 06 October 2026
Introduction to Artificial Intelligence (AI) and Machine Learning (ML)
- History of AI
- Types of AI
- Current situation and real-life examples
- Technological Basics
- Different learning / training Methods
- Example use cases
Data and Models
- Overview of core model architectures and data types
- Data splitting: training, validation, and testing phases
- Ensuring data quality, representativeness, and overcoming common bottlenecks
Specialised AI: How Task-Specific Models Work
- Specialized vs. General-Purpose AI: Understanding the key differences
- Step-by-step training process, using cancer diagnostics as a case study
- The roles of pre-training and fine-tuning
- Ensuring AI traceability and explainability in critical Tasks
General-Purpose AI: How Generative Models like GPT & Co. Work
- Fundamentals of Large Language Models (LLMs)
- Strengths and limitations of LLMs compared to specialized AI
- Introduction to LLMs acting as autonomous agent
Risk Management / Validation for AI/ML Solutions
- Applying ICH Q9 QRM to AI/ML
- QRM throughout the System Life Cycle
- Thinking critically about AI-enabled Computerized Systems
- Using the AI Maturity Model
- Achieving Data and Model Governance
Overview of AI/ML in Pharmaceuticals, Biotech and Medical Devices
- Challenges facing the life sciences industry in dealing with AI and ML
- GAMP® 5 meets AI: The GAMP AI Guide
- Use cases for AI in pharma and biotechGMLP (Good Machine Learning Practice): An SDLC for AI/ML
Annex 22 / AI Conference 2025 - 07-08 October 2026
Overview of AI in GxP: Capabilities & Opportunities
- General introduction
- Brief introduction to AI & ML
- Drivers for using AI & ML in pharma
- Regulations and guidance
AI Limitations and Areas of Concern
- Current situation
- What do you need to watch out for?
- What are the risks?
Current regulatory Situation – The Current Status in Regard to EU GMP Guide Annex 11 and Annex 22 - and Expectations in the Context of an Inspection
Risks and Limitations of Large Language Models (LLMs): A Critical Discussion
- Open-Source vs. Closed-Source: Transparency, control, and dependencies
- Bias in training data and its impact on results
- Data privacy and confidentiality when using generative AI
- Hallucinations: Why LLMs generate convincingly false information
Security Implications of AI for Pharma Manufacturing
- Overview: State-of-the-art cyber security & resilience
- AI as a threat versus AI as an opportunity
- Examples AI-based attacks
- Urgent call to action for the industry
Inspection Readiness
- Regulatory developments from an industry perspective
- What are inspectors looking for?
- How to prepare ahead of time
- Practical insights in inspection preparation
AI Strategy & AI Governance – The Big Picture
- What do organizations need to consider when approaching AI in GxP
- How does AI strategy interconnect within a typical corporate strategy layout
- What governance functions are relevant and how do they integrate
- How can enabling elements like data, Quality, and technology be activated
GenAI Platform: A Technical and Compliant Approach for Complaint GenAI Usage in a GxP-Regulated Environment
- Platform mindset: Embracing a platform-oriented approach to leverage GenAI capabilities effectively.
- Democratization of Technology: Ensuring easy access to GenAI tools across the enter-prise, empowering end-users to innovate responsibly.
- Quality Risk Management: Implementing a comprehensive quality risk management framework to evaluate and mitigate potential quality impacts associated with GenAI usage.
AI-Based Assistance Software to Increase Production Efficiency in a GMP-Regulated Environment
- Maintenance Challenges
- How knowledge databases work
- Framework Conditions for the Use of the AI-Based Assistant
- Quality Control of Knowledge Entries
- Benefits, Impact, and Outlook
Digitalization/Automation as the Basis for the Efficient Use of AI in QA and QC
- Basics for QC & QA on IT framework - digitalization - automation - use of AI
- Generation of raw data and data systems
- Real-life automation examples and AI examples from QA & QC
AI in Maintenance – Between Expectation and Reality
- Critical Reflection on AI in Maintenance
- Expectations and roadblocks
- Limits of data driven Models
- Pragmatic alternatives
- Practical experience at CSL
AI in the Pharmaceutical Industry: Regulations, Quality Assurance and Practice
- Regulatory requirements (EU AI Act, Annex 11, 22, Chapter 4, Part 11)
- AI governance: managing risks associated with non-GxP systems in a GxP environment
- Validation: CSV as a holistic approach for compliant AI
- Human-machine interaction: Human-in-the-loop vs. human-centric concept
- Practical examples: Benefits of AI in quality assurance / Thoughts on the potential and risks of AI for patient care
- Outlook: The future role of AI agents
AI and Data Integrity: Limitations (Guardrails) and Opportunities in a Regulated Environment
- Why data integrity is critical in the GMP environment – an overview of regulatory requirements
- Challenges posed by AI – risks to data integrity in generative and agentic systems
- Agentic AI in a regulated environment – the limits and potential of AI
- (Guardrails as a solution)
- Outlook – future developments/trends
Beyond Hype and Shadow AI: Building Trusted, Compliant AI in GxP
- Deep domain and process knowledge to correctly frame AI use cases
- Reliable data quality and availability as the bedrock of compliance
- Robust governance frameworks (e.g., the 4G model: Guard, Govern, Guide, Gate) to align with GxP, ISO, GDPR, and FDA expectations
- Practical use cases, such as applying AI to product risk management, showing how copilots can accelerate risk assessments while maintaining audit-readiness