AI in pharmacovigilance: where human review still matters

AI can help pharmacovigilance teams draft narratives, summarize source information, classify text, identify missing fields, and create review checklists. Used carefully, it can reduce repetitive work and help teams focus on judgment.

But PV is not just text production. It is a regulated safety workflow. The output must be accurate, traceable, privacy-aware, and reviewed by a qualified person before it influences a decision.

Where AI can help today

• Drafting first-pass summaries from approved source material.
• Creating quality checklists for case narratives, literature reviews, or signal meeting preparation.
• Standardizing internal training examples and role-play scenarios.
• Helping reviewers compare an output against source documents and required fields.

Where human review is non-negotiable

• Medical meaning, seriousness, causality, expectedness, and clinical context.
• Final narrative accuracy and alignment with source documents.
• Signal interpretation, benefit-risk thinking, and regulatory submission decisions.
• Privacy checks, data minimization, and approval to use any real case information.

A safer operating model

Treat AI as a drafting and organization layer, not an accountable reviewer. Keep a human in the loop, define what data can be used, keep records of prompts and sources when appropriate, and do not use AI outputs as final regulated decisions.

For training, this is a powerful message: life-science professionals do not need to fear AI, but they do need to understand oversight, validation thinking, and documentation discipline.