PiVily Quarterly Report
Scale stopped being theoretical
Regulatory networks published evidence of operating scale in real-world data and AI, raising the standard for governance, capability and useful interpretation.
The quarter in one reading
Q2 2026 showed that data and AI were no longer edge programmes in medicines regulation. The harder question became whether operating models could preserve scientific rigor as access, volume and use cases expanded.
This retrospective edition was reconstructed from official materials and completed on 11 August 2026. It describes the quarter; it does not imply contemporaneous publication.
Material signals
What moved—and why it mattered.
DARWIN EU reported a materially larger evidence network.
EMA's June 2026 report said DARWIN EU had reached 40 data partners across 18 European countries, access to data from around 250 million patients, and 88 completed or ongoing studies across 108 research topics.
EMA: Real-world evidence: guidance, reports and catalogues EMA: DARWIN EU key figures and study outcomes
Scale strengthens the range and timeliness of questions regulators can examine. It also makes provenance, method selection, quality and interpretation more—not less—important.
Can the team distinguish evidence-network scale from the fitness of a specific study for a specific safety decision?
The European regulatory network documented movement from concept to practice.
EMA and HMA published the 2025 AI Observatory report in June 2026, describing activity across guidance, regulatory applications, collaboration, system automation, knowledge mining and data handling.
Documented activity is not equivalent to validated performance. The value of the Observatory is the system-wide learning loop it creates for priorities, gaps and future guidance.
Does the organisation have an equivalent mechanism to convert AI experience into updated controls and capability—not just adoption metrics?
AEMS increased the immediacy of public adverse-event information.
FDA's AEMS transition included real-time publication across product categories and planned migration of historical data, APIs and analytics capabilities during 2026.
More timely data shorten the distance between a report, an external analysis and a public question. Scientific explanation must be ready at the same speed as access.
Who owns rapid, evidence-calibrated interpretation when an external party raises a pattern from public safety data?
Next-quarter watch
Questions to carry forward.
- How regulator-led RWE informs concrete safety and benefit-risk decisions
- More specific assurance expectations for AI used in PV
- The effect of faster public data on signal communication and stakeholder trust
Method & source register
Trace the reading back to the record.
PiVily selects developments for system-level relevance to human pharmacovigilance. Facts are attributed to official publishers; interpretation is editorial. This is not an exhaustive jurisdictional change log or organisation-specific advice.