AI in Pharmacovigilance vs Human Review: Which Is Better for Signal Management?

The question for pharmacovigilance leaders is not whether AI or people should manage safety signals. It is which tasks AI can support, where expert review must remain central, and how to demonstrate that the combined process is reliable.

For most organisations, the practical model is AI-assisted, human-accountable pharmacovigilance. AI can help teams process information and prioritise review. It cannot take responsibility for clinical judgement or a marketing authorisation holder’s regulatory obligations.

Where AI can support signal management

Signal management in pharmacovigilance brings together data from individual case safety reports (ICSRs), literature, studies and other sources. AI can help organise and review this information, particularly where teams face high volumes or varied formats.

Potential uses include:

• Case intake: extracting structured fields from forms, emails and narrative reports.
• Triage: prioritising cases for review based on factors such as seriousness or missing information.
• Duplicate detection: identifying reports that may describe the same patient or event.
• Coding support: suggesting MedDRA terms for reviewer confirmation.
• Literature screening: surfacing potentially relevant articles for assessment.
• Signal detection support: grouping cases, highlighting patterns and prioritising signals for further investigation.

These are support functions, not final decisions. An algorithm can help a team notice a pattern; it cannot establish that the pattern is clinically meaningful or that a medicine caused an event.

What human reviewers must retain

Human oversight matters most when interpretation can affect patients, reporting obligations or a product’s benefit-risk assessment.

A qualified reviewer should remain responsible for decisions such as:

• Whether a report is valid and sufficiently complete.
• How seriousness, expectedness and clinical context should be assessed.
• Whether evidence supports a causal association.
• Whether a potential signal warrants validation, escalation or regulatory action.
• How new information affects the product’s benefit-risk profile.
• Whether and how to communicate with regulators, healthcare professionals or patients.

AI may suggest a causality category or summarise case evidence, but the responsible professional must evaluate that output against the full case and applicable procedures. The same principle applies to signal decisions: automated ranking can support prioritisation, but signal confirmation and action require expert assessment.

In India, the CDSCO and PvPI Pharmacovigilance Guidance Document for Marketing Authorisation Holders describes responsibilities for case processing, causality assessment, signal management, quality systems and inspections. It also states that responsibility for the pharmacovigilance quality system remains with the MAH, including where activities are outsourced.

Indian language reporting needs specific testing

Digital reporting can increase the range and volume of information available to safety teams. The Indian Pharmacopoeia Commission has released the iOS version of the ADR-PvPI 2.0 mobile app, extending digital options for reporting adverse drug reactions.

AI-enabled tools could help translate or structure reports submitted in Indian languages. But general multilingual performance is not enough. A system needs evaluation on the clinical language it will actually encounter, including regional expressions, spelling variation and mixed-language narratives.

Errors can change meaning. A tool might lose a symptom’s timing, miss a negation, misread a medicine name or make an uncertain statement sound definitive. Preserve the original report, record any translation or transformation, and route unclear or high-impact cases to a suitably trained reviewer. The IPC’s consumer reporting forms in Indian languages illustrate why language-specific validation matters.

Validation, records and inspection readiness

Before deploying AI, define its intended task and the consequences of an incorrect output. An intake tool that suggests fields for review has a different risk profile from a system that can affect case prioritisation or signal escalation.

Validation should be specific to the tool’s intended use, data and operating context. Decision-makers should expect to see:

1. Representative test data, including relevant products, report types and languages.
2. Performance measures that reflect the task, such as missed cases and incorrect classifications, not accuracy alone.
3. Defined thresholds for accepting, escalating or rejecting outputs.
4. Human-review procedures for uncertain, serious or otherwise high-impact cases.
5. Ongoing monitoring for performance changes, data drift and recurring errors.
6. Change control for model, vendor, configuration or workflow updates.

Keep an audit trail that allows a reviewer or inspector to reconstruct what happened: the source material, model and version, output, reviewer changes, final decision and any escalation. The EMA reflection paper on AI in the medicinal product lifecycle identifies validation, monitoring and documentation as MAH responsibilities when AI supports post-authorisation activities, including pharmacovigilance.

The inspection environment reinforces the need for evidence. EU GVP Module III Revision 2 took effect on 10 September 2026. It strengthens the risk-based approach to inspections and addresses inspection of subcontracted pharmacovigilance activities. For an AI-supported process, organisations should be able to explain the system’s role, show its validation and change history, and demonstrate how the MAH oversees relevant vendors and subcontractors.

Privacy considerations across India and the EU

ICSRs may contain health information and details that identify, or could help identify, patients and reporters. Privacy safeguards should therefore be built into the workflow, not added after a system has been selected.

For EU operations, health data is special-category personal data under GDPR. Organisations need an applicable legal basis and condition for processing, along with appropriate safeguards. Pseudonymisation can reduce exposure but does not automatically make an identifiable case anonymous or remove GDPR requirements. Assess vendor access, international transfers, retention and deletion as part of the data-protection review. The GDPR text sets out the relevant principles and obligations.

For Indian operations, the Digital Personal Data Protection Act, 2023 is relevant to planning AI workflows involving personal data. As of October 2026, the Act’s core provisions are scheduled to commence on 13 May 2027 under the phased commencement framework. Organisations should prepare by mapping data flows, limiting inputs to what is needed, defining retention and access controls, and reviewing vendor and cross-border arrangements against applicable requirements.

Anthropic’s Life Sciences Verification Program, announced in September 2026, is a beta access programme for eligible life-sciences organisations. Access to an AI model through such a programme should not be treated as regulatory approval, pharmacovigilance validation or confirmation of compliance with GDPR or Indian data-protection requirements. Any use with safety data still requires the organisation’s own privacy, security, validation and governance review.

How to decide where to deploy AI

A practical deployment decision starts with the task, not the technology.

• Start with bounded support tasks. Intake extraction, duplicate suggestions and literature prioritisation can be suitable pilots when a reviewer verifies results.
• Assess the cost of error. Consider what happens if the system misses a serious event, misclassifies a case or overlooks a potential signal.
• Test on your own data. Include Indian clinical language where relevant, and test performance across the populations, products and sources in scope.
• Keep accountability explicit. Document who reviews outputs, who can override them, and who makes the final regulatory or clinical decision.
• Make the process inspectable. Ensure the organisation can retrieve records, explain changes and demonstrate oversight of service providers.

The most appropriate use cases are those where AI reduces repetitive work while leaving professionals time to assess evidence. If a use case depends on unverified outputs or makes responsibility unclear, pause and redesign the workflow.

The decision is about the workflow

AI can help pharmacovigilance teams manage information at scale and bring potential patterns to review sooner. Human expertise remains essential for causality, signal confirmation, benefit-risk judgement and regulatory accountability. The right balance depends on a validated task, reliable records, appropriate privacy controls and clear oversight.

The draft programme for Pharmacovigilance India 2027 includes a panel on signal management in the age of AI. The one-day conference takes place in Chennai on 29 July 2027, bringing senior PV leaders together to discuss practical regulatory and operational challenges.