AI is becoming essential, not optional, in modern PV operations
Artificial intelligence is reshaping pharmacovigilance across case intake, triage, causality assessment, periodic safety reporting, REMS reporting, literature review, and regulatory intelligence. As adoption grows, organizations must balance automation against AI and decide when to build versus buy.
In a highly regulated environment where accuracy is critical, teams need to understand both the value and the limitations of AI. That means knowing where AI accelerates work, where human oversight is non-negotiable, and how to validate systems that behave probabilistically rather than deterministically.
This white paper outlines real-world and emerging AI use cases across PV, and introduces the “centaur model,” where human expertise and AI work collaboratively to improve speed, quality, and decision-making.
Four domains where AI is already delivering value
Each area combines AI-driven efficiency with human review to meet the compliance and accuracy standards PV demands.
Periodic Safety & REMS Reporting
DSURs, PSURs, and REMS assessments are data-intensive and template-driven, making them prime candidates for AI-assisted drafting, data aggregation, and consistency checks.
- Automated data aggregation across multi-source datasets
- Draft narrative generation from structured inputs
- Inconsistency and change detection across submissions
Case Processing
Where volume, variability, and tight regulatory timelines intersect, AI accelerates narrative drafting, triage, PT coding, and data entry from unstructured sources.
- Rapid ICSR triage and prioritization
- MedDRA Preferred Term suggestion from ambiguous verbatim
- OCR and NLP extraction from unstructured source documents
Regulatory Intelligence
Monitoring evolving requirements across jurisdictions is resource-intensive. AI tracks updates, analyzes changes, and streamlines submission-package preparation against regional standards.
- Cross-jurisdiction regulatory change tracking
- Cover letter and eCTD package generation
- Completeness validation against regional requirements
Operational Support
Beyond core safety workflows, AI is embedded in daily operations: meeting summaries, translation, searchable SOP libraries, and pattern recognition across signal management activities.
- Meeting summarization and action-item tracking
- Multilingual translation with human validation
- SOP libraries as interactive knowledge systems
The centaur model.
The future of PV lies in intentional collaboration between humans and AI. AI handles analysis, interpretation, and insight generation. Humans retain judgment, oversight, and accountability. The goal is augmentation, not replacement, redefining how expertise is applied in an AI-enabled environment.
- Validate continuously. AI models behave probabilistically, so validation is an ongoing assurance process, not a one-time gate.
- Preserve clinical judgment. Causality assessment and safety-critical decisions require human accountability that AI cannot replicate.
- Match tool to task. Automation handles rule-based, recurring work. AI handles context-driven, interpretive scenarios. The best PV operations use both.
- Build vs. buy deliberately. Commercial tools offer speed and reliability. In-house solutions offer flexibility and strategic value for niche use cases.
Implement AI in PV with efficiency, consistency, and human oversight at the core.
Get the full white paper for use case detail, IT security and validation considerations, and a framework for deciding when to build versus outsource.
Download the Full White PaperReferences
Full citation list included in the downloadable white paper. Sources include the CIOMS Working Group report on AI in pharmacovigilance, the FDA’s guidance on AI to support regulatory decision-making, the EMA reflection paper on AI in the medicines lifecycle, the ICH M15 guideline on model-informed drug development, and Mollick, E. (2024), Co-intelligence, Penguin.
