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We now have AI systems capable of scanning millions of adverse event records, extracting safety signals from social media posts, and flagging a potential drug-reaction pattern before a human reviewer has even opened the case. So why, in 2026, are regulators still expressing serious reservations about AI in pharmacovigilance?
The answer isn't a lack of technology. It's a problem hiding in plain sight: the data feeding these systems.
Signal Detection: The Foundation of Proactive Pharmacovigilance
For most of pharmacovigilance's history, signal detection was a back-office function. Teams ran disproportionality analyses, proportional reporting ratios, reporting odds ratios, Bayesian methods like EBGM, on spontaneous reporting databases, reviewed the outputs periodically, and escalated anything that crossed a threshold. It was systematic, it was compliance-driven, and by modern standards, it was slow.
That model no longer fits the scale of the problem. Adverse event reporting volumes have grown substantially, and safety teams now pull from a far wider range of sources than ever before. This has pushed signal detection from the periphery to the centre of Global Pharmacovigilance strategy. It is no longer just about detecting problems after they emerge, regulators and organisations alike now expect teams to anticipate risks, spot emerging patterns early, and act before patient harm accumulates. This is what "proactive pharmacovigilance" actually means: not a philosophy, but an operational requirement that demands smarter infrastructure.
What Regulators are now Requiring?
The regulatory community has been watching AI's entry into pharmacovigilance carefully, and in the past eighteen months the expectations have become considerably clearer.
In January 2025, the FDA released draft guidance on using AI to support regulatory decision-making, introducing a risk-based credibility assessment framework that requires organisations to define the intended use of an AI system, document its performance limits, and maintain auditability throughout its lifecycle. In January 2026, the FDA and EMA jointly published ten Guiding Principles of Good AI Practice in Drug Development, reaffirming that human oversight is non-negotiable regardless of how capable the underlying model is.
Separately, the FDA's E2B(R3) compliance deadline in April 2026, mandating structured, digital ICSR reporting across the life sciences industry, is itself a prerequisite for better AI inputs. You cannot feed clean data into a model if the incoming ICSRs are still arriving in inconsistent, legacy formats.
The Foundations matter more than the Tools
The industry has arrived at a pivotal moment. The AI tools available for pharmacovigilance signal detection in 2026 are genuinely powerful.
But the transformative potential of these tools will only be realised if the data they depend on is fit for purpose. Patient safety is not served by detecting signals more quickly from a biased, underreported, inconsistently coded dataset. It is served by building systems that know their own limits, are transparent about what they do and do not see, and keep qualified human judgement at the centre of every consequential decision.
2026 should be the year the PV community stops asking "which AI tool should we deploy?" and starts asking "is our data ready for it?"
At COD Research, we help pharmaceutical, biotech, and medical device companies build PV operations that are as rigorous about data quality as they are about technology.