Pharmacovigilance generates large volumes of semi-structured text: case narratives, literature, correspondence. That makes it a natural candidate for machine learning. It is also a domain where errors can affect patients, which means how AI is introduced matters as much as whether it is.
Where AI can help
- Triage of incoming reports to prioritise potentially serious cases.
- Literature screening to surface likely relevant articles for human review.
- Suggested coding for medical terms, confirmed by a coder.
- Duplicate detection and consistency checks across records.
Where caution is essential
Causality assessment, seriousness decisions and regulatory reporting judgements involve clinical reasoning and accountability. Systems may inform these steps; they should not silently replace the qualified person responsible for them.
Guardrails that matter
- Human oversight: a named, qualified reviewer accepts or rejects every consequential output.
- Explainability: reviewers can see why a suggestion was made, not just what it is.
- Validation: performance is measured against defined acceptance criteria on representative data, and re-checked when data or models change.
- Data privacy: personal and health data are minimised, protected and used only for defined purposes.
- Traceability: model versions, inputs and decisions are logged and auditable.
Measure what you rely on
A tool that is right on average can still fail on exactly the rare, serious cases that matter most. Evaluation should look at performance on high-risk subsets, not just overall accuracy, and should plan for monitoring after deployment.
This article is for general information only and is not medical, regulatory or legal advice.
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