Consulting Services

Is your PV AI audit-ready?

The FDA, EMA, and MHRA are already inspecting AI systems in Pharmacovigilance, and critical findings are being issued. We help organizations using or developing AI applications to prepare for intense regulatory scrutiny.

The New Reality of AI Inspections

Traditional PV Focus

SOPs, training records, case quality, ICSR integrity, and submission timelines.

NEW AI Audit Requirements

  • Algorithm validation & data provenance
  • AI Governance charter & accountability
  • Human-in-the-loop review logs

Regulatory Expectations

FDA, EMA & MHRA: The 4 Fundamental Pillars

Inspectors trace your AI systems against these core principles to establish compliance and control.

1. AI Governance

  • Multidisciplinary supervision
  • Clear accountability structure
  • Defined decision rights

2. Transparency

  • Traceable audit trails
  • Algorithm explainability
  • Proven data provenance

3. Validation

  • Fit-for-purpose prospective testing
  • Continuous performance monitoring
  • Strict version control

4. Human Oversight

  • Human-in-the-loop (HITL) processes
  • Demonstrable human review capability
  • Formally qualified reviewers

High-Risk Areas

Where scrutiny is intensifying in 2025

Inspectors are actively looking for specific implementation gaps in AI-powered workflows.

ICSR Workflows

AI analysis of unstructured data (emails, call centre logs) without formal validation of extraction accuracy and entity recognition.

Chatbots & Virtual Assistants

Patient-facing AI providing safety or product information without adequate medical review routing or disclaimers.

Literature Monitoring

AI filtering thousands of articles but potentially missing relevant safety publications (unmeasured false negative rates).

Signal Detection

Statistical signals generated autonomously by AI algorithms without a framework for clinical and biological plausibility.

Computer System Validation (CSV)

Existent CSV gaps for AI-enabled systems: missing IQ/OQ/PQ, lack of periodic review, and uncontrolled change management.

Inspector Focus

Common Inspection Findings

We've analyzed recent regulatory actions to identify the most frequent pitfalls organizations face when rapidly deploying AI.

CRITICAL

Shadow AI

AI tools implemented without formal quality approval, unknown to the PV team, or lacking validation.

Inspector Rationale System out of control
CRITICAL

Retrospective Validation

AI models deployed into production first, and validated later (or never).

Inspector Rationale Prospective validation mandatory
MAJOR

Lack of Version Control

Algorithm updates (prompt changes, model swaps) pushed without documentation or revalidation.

Inspector Rationale Change control violation
MAJOR

Human Review Logs

Inability to demonstrate with system logs that qualified technicians reviewed and approved AI outputs.

Inspector Rationale Lack of oversight evidence
MAJOR

Training Data Gaps

Unknown data provenance, lack of representativeness testing, or failure to evaluate biases.

Inspector Rationale Unproven data quality

The QPPV's Dilemma

Under European and UK law, the Qualified Person for Pharmacovigilance (QPPV) retains ultimate responsibility for the PV system's integrity-including any AI components driving safety decisions.

"You cannot outsource accountability."

QPPVs must have visibility into AI tool selection, validation metrics, and ongoing oversight to legally certify system compliance in the PSMF.

Third-Party Risk

Securing the Vendor Ecosystem

Most pharma companies procure AI solutions rather than build them. Agencies expect you to audit your technology vendors with the same rigor as clinical CROs.

  • Vendor Qualification Audits specific to AI development lifecycles.
  • Review of SaaS provider's change control (Are they silently pushing updates?)
  • Contractual guarantees for data isolation and privacy.
  • Ensuring vendor algorithms weren't trained on competitor proprietary data.

Protect your compliance record.

Our AI validation experts conduct gap analyses and prepare your Pharmacovigilance teams to confidently defend their AI implementations.