AI that removes the data entry, not the accountability
PVgenix applies AI where pharmacovigilance genuinely loses time — extracting case data from PDFs and emails, proposing MedDRA terms, screening literature — and stops exactly where regulated judgment begins. Every AI-assisted step is scored, reviewable, and on the audit trail.
The AI question PV teams actually have
Nobody in pharmacovigilance needs convincing that case processing is labour-intensive. The hesitation about AI is not about capability — it is about accountability, and about what happens at an inspection when a regulator asks who made a decision.
Qualified people doing clerical work
Reading a PDF and re-typing patient, product, event, and reporter fields into a form is not a use of pharmacovigilance training — but it consumes a large share of case processing time.
Literature volume that outpaces reviewers
A single search cycle can return over a thousand hits, of which a handful are genuine ICSRs. Screening all of them manually is the only option in most systems.
"Black box" AI is not inspectable
A model that produces an answer with no confidence signal, no reviewable evidence, and no record of what it did cannot sit inside a GxP process.
Automation that quietly takes decisions
If a system can auto-close a serious case or set causality without a qualified person confirming it, the accountability model breaks — regardless of how accurate the model is.
PVgenix draws the line explicitly: AI proposes, a qualified person decides. Extraction, coding, and screening are AI-accelerated with per-field confidence scoring and mandatory review gates for anything low-confidence or serious — and causality, expectedness, and seriousness sign-off remain with your PV personnel by design, not by policy.
Where AI is applied — and where it stops
AI is used for extraction, proposal, and triage. It is not used to make regulatory or safety determinations autonomously.
Extraction from unstructured sources
Case data is extracted from PDFs, emails, and free-text narratives, segmented by domain across patient, product, event, lab, and reporter data rather than as one undifferentiated block.
See how it worksConfidence scoring per field
Each extracted value carries a confidence score, and low-confidence items are routed automatically to human review instead of flowing through unnoticed.
See how it worksAI-assisted MedDRA coding
Terms are proposed against the admin-controlled dictionary version, with manual override available on every term and the override recorded on the audit trail.
See how it worksAI-assisted narrative generation
A draft narrative is generated from the structured case data, then reviewed and edited by a qualified user before the case proceeds.
See how it worksLiterature screening and triage
Titles and abstracts are screened for relevance against your search strategy, and candidate ICSRs are flagged for a reviewer to confirm against the four ICH E2D criteria.
See how it worksStraight-through processing, scoped
Configurable straight-through processing is applied only to qualifying low-risk case types, with thresholds set per tenant and case type; human review is the default for serious or low-confidence cases.
See how it worksRules-based routing and triage
Deterministic, configurable rules — not a model — decide how a case is routed and prioritised, so the behaviour is inspectable and repeatable.
See how it worksNot AI: the regulated determinations
Causality, expectedness/listedness, seriousness sign-off, and final case approval are human decisions made by your qualified PV personnel. AI does not make them and cannot override them.
See how it worksHow the human-in-the-loop model is enforced
"Human oversight" is easy to claim and hard to evidence. These are the mechanisms that make it structural rather than procedural.
AI cannot close a regulated decision
There is no configuration in which AI performs a causality, expectedness, or seriousness sign-off. Those states can only be set by an authorised user.
AI activity is on the audit trail
What the AI proposed, what confidence it assigned, what the reviewer changed, and who approved it are all captured — so the decision chain is reconstructable at inspection.
You set where the gates are
Confidence thresholds and review requirements are configured per tenant and per case type, so the level of automation is your decision, documented in your configuration.
Reviewable evidence, not just an answer
Extracted values are presented alongside the source context so a reviewer verifies against the original document rather than trusting the output.
Deterministic where determinism matters
Reportability, routing, and the regulatory clock are driven by configurable rules rather than inference, because those outcomes must be repeatable and explainable.
Fits the qualification model
Because AI assists rather than decides, AI-assisted steps sit inside the same review-and-approval controls you already qualify, supported by the IQ/OQ/PQ documentation package.
Answers before the demo
The questions that come up in most evaluations. If yours is not here, ask it on the call.
Browse the full FAQIn PVgenix, AI is applied to the high-volume clerical work: extracting case data from unstructured sources such as PDFs, emails, and narratives; proposing MedDRA terms; drafting narratives from structured case data; and screening literature search results for relevance and candidate ICSRs. Each output carries a confidence signal and is presented to a qualified user for review, edit, and approval before the case proceeds.
No. AI assists intake and data entry; it does not make final regulatory or safety determinations autonomously. Causality assessment, expectedness/listedness determination, seriousness sign-off, and final case approval remain with the client's qualified PV personnel. All AI-assisted steps are captured in the audit trail.
Human-in-the-loop means the AI produces a proposal that a qualified person must review and approve before it has any effect on the regulated record. In a GxP context this matters because accountability has to rest with a person: the audit trail must show what was proposed, what was changed, who approved it, and when. PVgenix enforces this structurally — regulated determinations can only be set by an authorised user.
Straight-through processing allows a case to move through the workflow with reduced manual intervention. In PVgenix it is configurable and applied only to qualifying, low-risk case types, with thresholds set per tenant and case type. Human review remains available throughout and is the default for serious or low-confidence cases, so the scope of automation is an explicit, documented client decision rather than a system default.
Accuracy varies by source quality, document type, and field — which is precisely why extraction is confidence-scored per field rather than presented as a single trust level. Low-confidence items are routed to human review automatically, extracted values are shown alongside the source context for verification, and a reviewer can edit any field. Because nothing reaches the regulated record without human approval, an extraction error is a correction during review, not a compliance event.
The human-in-the-loop design is intended to keep AI inside controls you already qualify. Because AI-assisted steps produce proposals that pass through existing review-and-approval gates, and because reportability and routing are driven by deterministic configurable rules rather than inference, the qualification approach follows the same review-gate logic as manual processing. PVgenix provides the IQ/OQ/PQ documentation package to support that work; qualification execution against your environment and SOPs is client-led.
See AI-assisted processing on your case types
Request a demo and we will run your document types through extraction, confidence scoring, and the human review gate.