Pharmacovigilance has a volume problem that predates any conversation about artificial intelligence. Case intake grows, reporting obligations do not relax, and the people qualified to make medical judgements are the constraint — not the people available to type.
That shape matters, because it tells you where automation legitimately helps and where it does not. The work that scales badly is intake, duplicate detection, coding and narrative drafting. The work that must not be automated is causality assessment and the decision about whether a signal is real.
This is how we approach pharmacovigilance, including where we think the line sits.
Case intake and duplicate detection
Intake is where volume bites first. Cases arrive through multiple channels in inconsistent formats, and the first task is establishing whether a case is new, a follow-up to an existing case, or a duplicate arriving by a second route.
Duplicate detection was always statistical. Matching on patient characteristics, event, dates and reporter across inconsistent data is a pattern problem, and it was being done by algorithm long before anyone branded it AI. Treating it as a probabilistic step with a human reviewing the uncertain matches — rather than as a deterministic rule that is either right or wrong — produces better outcomes and a more honest validation approach.
What has to be validated is not that the matching is perfect. It is that the thresholds are justified, the uncertain cases reach a human, and the failure mode is biased towards creating a duplicate rather than merging two distinct cases. Merging two real cases loses a report; creating a duplicate is recoverable.
Narrative drafting, and what stays human
Case narratives are structured prose assembled from structured data, which makes them among the most automatable documents in regulated life sciences. The narrative has to be complete, consistent with the case data, and readable. Those are tractable requirements.
What does not move is the medical assessment. Causality, expectedness, seriousness and the clinical judgement in a company comment are decisions a qualified person makes and signs. A system that drafts the descriptive narrative and leaves those fields for the assessor is working correctly. A system that proposes a causality assessment is doing something materially different and should be treated accordingly.
The distinction is worth holding firmly, because it is easy to erode by convenience. safety narratives is how we handle narrative drafting with the source traceability that makes each statement checkable against the case record, and safety narratives is the practice.
Aggregate reports
PSURs, PBRERs and DSURs consume disproportionate effort relative to their novelty. Much of each document is assembled from the same sources every cycle — exposure data, cumulative tabulations, literature, previous conclusions — and much of the authoring effort goes into reassembling material that has not changed.
The reusable part is the compilation and the consistency checking. The part that is not reusable is the benefit-risk evaluation, which is the point of the document and the section a reviewer reads first.
Getting this split right changes the resourcing conversation. If qualified assessors are spending most of their time assembling rather than evaluating, the document is produced by the wrong people. medical writing covers the authoring practice.
Signal detection and the judgement that cannot move
Statistical signal detection is established practice — disproportionality measures on spontaneous reporting data have been used for years, and the methods are well understood including their limitations.
What matters is what happens to a statistical signal. A disproportionality measure crossing a threshold is a prompt to look, not a finding. The assessment of whether a signal represents a real safety issue requires clinical knowledge, awareness of confounding, reporting bias and the characteristics of the data source. That assessment is the work, and it is where automation has nothing useful to offer.
So the honest framing for any tool in this space is that it changes what reaches a qualified assessor and in what order. It does not change who decides. A vendor describing AI-driven signal detection should be asked what the human does, and the answer should be substantive. AI in pharmacovigilance signal detection goes further.
Validating the systems, and the AI inside them
PV systems are GxP systems and validate as such: intake completeness, duplicate handling, coding, the reporting clock and its triggers, submission generation, and the audit trail across all of it. The reporting clock deserves scripted, boundary-focused testing — a day-zero calculation that is wrong produces late reports, which is among the more serious findings available.
Where the system includes AI, the questions extend. The draft EU GMP Annex 22 — published for consultation in July 2025, not yet in force, and still the clearest statement of direction — frames them usefully: what the model is approved for and what it is not, what evaluation set gates a release, what safeguards sit on outputs, how drift is monitored, and what happens when the AI is unavailable.
That last point is specific in a PV context. Reporting obligations do not pause because a model is offline, so the fallback has to be a working manual path rather than a documented intention. AI validation services is the practice.
Where to start if volume is the problem
Intake and duplicate detection, in that order. They are the highest-volume, lowest-judgement steps, they are where backlog accumulates first, and improving them frees qualified people for assessment rather than replacing them.
Narrative drafting next, with the medical assessment fields explicitly excluded. Aggregate report compilation after that.
Signal assessment last, and arguably not at all. book a demo if you want a view on where the constraint actually is in your own process — it is not always where the backlog is visible.
Where to go next
Explore GxP Copilot for AI-native validation, TraceDraft for source-traceable clinical documentation, or book a demo to see either on your own data.
