Artificial Intelligence in Pharmacovigilance

From use cases to inspection-ready governance, validation and human oversight
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Artificial Intelligence in Pharmacovigilance

online live

Training features

Fully online

All sessions of this unique course online

Live interaction

Live interaction options between the delegates and trainer

On any device

Available via mobile, tablet or laptop

Course overview

This is not a general AI awareness course. It focuses on how AI changes regulated PV work in practice: process design, human oversight, validation, vendor control, data integrity, audit trails, accountability and inspection readiness.

Artificial intelligence will not remove accountability from pharmacovigilance. It changes where accountability must be designed, documented and demonstrated.

This course helps participants understand how AI can support PV work while maintaining regulatory compliance, patient safety, data integrity and inspection readiness.

The course focuses on practical, inspection-ready AI adoption in pharmacovigilance. It should not present AI as a generic technology trend, but as a controlled change to regulated PV work: data flows, decision making, oversight, validation, governance, vendors and accountability.

meet the training leader
Reinhold Schilling
Head of Global Pharmacovigilance, EU-QPPV
Wörwag Pharma, Germany
key training

Topics

From manual PV to automation and AI-enabled PV: current status, realistic maturity levels and lessons learned from implementation.

The PV hybrid model: how human expertise, automated workflows and AI-supported decisions can work together without losing accountability.

AI use cases in PV operations: intake, triage, duplicate detection, literature screening, MedDRA coding support, narrative drafting, quality control and workload prioritisation.

AI in signal detection and signal management: prioritisation, pattern recognition, signal narratives, evaluation support and the limits of automation.

Human oversight in practice: decision rights, escalation logic, competence requirements, audit trails, review thresholds and documented rationale.

AI governance in GxP and PV processes: policies, risk classification, lifecycle control, change governance, model monitoring and accountability.

Validation and qualification of AI-enabled PV systems: GAMP 5 thinking, EU GMP Annex 11, draft EU GMP Annex 22 considerations and practical validation packages.

Vendor landscape and vendor oversight: selection criteria, capability assessment, data protection, contractual controls, performance monitoring and inspection readiness.

EU regulatory environment: GVP expectations, EMA/ HMA digital and AI direction, EU AI Act implications for PV use cases, and alignment with ICH principles where relevant.

Implementation roadmap: how to move from pilot projects to a controlled, scalable and auditable AI operating model.

Benefits of attending

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