AI Pharmacovigilance ICSR Processing: From Verbatim to Reviewed Case

Where AI adds practical value

Most pharmacovigilance intake still contains repetitive manual work: reading source narratives, identifying potential adverse events, extracting dates, finding patient and reporter details, entering product information, and checking whether the same facts appear consistently across the case.

The VigiHelp AI engine supports these tasks by extracting candidate fields from verbatim text, highlighting adverse events for MedDRA coding review, preparing structured case data, and supporting causality and quality-control workflows. The reviewer workflow changes from entering every field manually to verifying, correcting, and validating AI-prepared case data.

What should remain under human control

AI output should be treated as review support, not as an uncontrolled final decision. Pharmacovigilance professionals remain responsible for confirming case validity, interpreting ambiguous source data, approving MedDRA terms, assessing seriousness and causality, and deciding whether follow-up is needed.

A good system makes suggestions visible and editable. It should show the source evidence behind each field, make gaps explicit, and avoid turning model confidence into a substitute for medical judgment.

Traceability is the key design requirement

Traceability means a reviewer can move from each structured field back to the source phrase or document that supports it. Without traceability, automation creates a black box. With traceability, it becomes a productivity layer that reviewers can challenge and improve.

This is especially important for causality assessment, seriousness criteria, follow-up changes, and MedDRA coding choices. These decisions can affect expedited reporting, aggregate analysis, and authority communications.

Implementation metrics for PV teams

Teams evaluating AI pharmacovigilance ICSR processing should measure more than speed. Useful metrics include extraction accuracy by field, coding agreement with reviewers, number of quality issues found before submission, rework rate, audit-trail completeness, and reviewer time per case.

Practical proof points can include average processing time reduced by 85% across 100 test cases, 92% of critical fields detected correctly before human review, automated case quality review before validation, and detection of missing information needed for follow-up.

A pilot should include routine cases, complex narratives, follow-up cases, serious cases, ambiguous reports, and legacy case QC. That mix exposes whether the workflow helps only with easy intake or also supports real operational variability.