This shift exposes a painful gap: most regulatory teams have not been involved in AI tool development. These tools are often developed by technical or vendor teams, with limited input from the people responsible for regulatory filings. And yet, it is the regulatory team that will be held accountable during inspections and reviews.
Action point: Regulatory leaders must establish internal checkpoints, request traceability plans from vendors, and assert their role in AI risk governance. Documentation, not assumptions, will determine approval outcomes.
This introduces several systemic risks. First, invisible bias. AI may prioritize outcomes based on overrepresented groups while underreporting rare adverse events. It may also embed regional data disparities—for instance, under-representing pharmacovigilance standards in Asia or Latin America, despite global use.
Second, auditability is often missing. When an AI model generates a risk score or labels an adverse event, regulators want to know: what data fed the model? What logic or algorithmic path did it follow? Where is the version history? Many tools in use today cannot answer these questions—and when they are embedded in safety reporting workflows, that becomes a serious liability.
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Finally, the input data itself is often incomplete. Few tools incorporate feedback loops, third-party data, or non-structured inputs. As a result, the AI becomes blind to key contextual signals—just when precision matters most.
Action point: Establish structured audit trails for all AI use cases. Ensure that human review, input source visibility, and real-time annotations are part of your validation plan—not retrofitted under pressure
Before submitting any regulatory documentation that includes AI-generated content or decisions, sponsors must evaluate whether their internal and external teams are aligned on compliance. Here are three core questions that often reveal critical gaps:
Can your team explain, in writing, how the AI tool generates risk assessments or outputs—without relying on vendor language?
AI systems cannot be treated as black boxes. Internal teams need to be able to explain how the model works, what logic governs its decisions, and how outputs were validated in context. If regulatory teams can’t articulate this clearly, the submission may not survive scrutiny.
Does your audit trail include change history, retraining documentation, and governance logs for all AI components?
Too many validations are frozen in time. Regulators now expect sponsors to show continuous oversight: how models are updated, when, and why—along with proof that changes were reviewed by qualified personnel.
Have you validated performance and bias across diverse data sources—not just your clinical trial database?
Real-world generalizability is a key demand. If your AI system performs well in sponsor-owned datasets but collapses in external conditions, regulators will ask why that testing wasn’t done up front.
