Overview

This interactive workshop explores the evolving role of automation and artificial intelligence (AI) in signal management.

Participants will gain a foundational understanding of signal detection principles and how emerging technologies from machine learning to large language models (LLMs) are reshaping traditional workflows.

The workshop combines theoretical insights with practical exercises, regulatory considerations, and real-world use cases across industry and regulatory settings.

Attendees will leave with a clearer view of where automation and AI can add value, what risks to manage, and how to begin implementing AI-enabled solutions in signal management.

Featured topics

- Fundamentals of signal detection: Definitions, detection methods, and PV context
- Introduction to AI and automation: Terminology, models, and practical relevance
- Principles of LLMs and generative AI in PV
- Oversight of AI: Quality control, hallucination risks, bias mitigation
- Regulatory landscape: EU AI Act, FDA AI guidance, CIOMS WG outputs
- Intellectual property, confidentiality, and ethical considerations
- Strategic planning for AI implementation in PV processes
- Practical exercises and demonstration

Who should attend?

This workshop is designed for pharmacovigilance professionals seeking to understand and apply automation and AI in signal management. It is especially relevant for:
- Drug safety specialists and signal management teams looking to future-proof their practices
- Pharmacovigilance managers and QPPVs evaluating AI-based solutions
- Data scientists, PV analysts and IT professionals collaborating with PV teams on AI system implementation
- Quality assurance and compliance officers involved in system validation and oversight

Working knowledge of LLMs is recommended but not essential. Participants with prior exposure to PV signal detection will gain the most from the training, but those new to the topic will also benefit from the foundational content provided.

The course focuses on the use of automation and AI in signal management, a continuous activity throughout the product lifecycle, including clinical trials. While the topic will be addressed broadly, the primary emphasis will be on the post-marketing setting, where organisations can leverage larger data volumes and apply automation and AI more effectively.

Learning objectives

- Define key pharmacovigilance concepts related to signal detection, including qualitative and quantitative methods.
- Explain the fundamental principles behind automation and AI, including how LLMs work.
- Identify potential risks and limitations of AI systems, such as bias, hallucinations, and regulatory challenges.
- Interpret emerging AI guidance from EU, FDA, and CIOMS in the context of signal management.
- Evaluate AI use cases in pharmacovigilance from different organizational perspectives (small, medium, large pharmaceutical companies, regulators).
- Develop initial implementation strategies for AI/automation solutions, including validation and oversight requirements.
- Apply learning through group-based case scenarios using practical tools and platforms.

Program Committee

Jan Kolouch, PharmD CEO, Strategic PV Advisor
NextPV Services s.r.o., Czech Republic

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Workshop on Automation and Artificial Intelligence in Signal Management