Pharmacovigilance has traditionally required organizations to balance the urgency of reporting with the rigor of accurately detecting adverse event and product complaints. AI has the potential to ease this burden by enabling earlier pre‑signal identification, automated detection, faster case processing and expanded surveillance across both structured and unstructured data.
However, when rules change faster than your roadmap, there is a need for foundational principles that can guide companies as they develop approaches that are both effective and ensure continued compliance.
Navigating a Fragmented Regulatory Landscape
AI’s rapid evolution has left regulators working to keep pace with technological change. As a result, global guidance on the use of AI in safety monitoring remains fragmented, reflecting varying regional standards. In a recent IDC Global research survey, 48% of the participants indicated regulatory concerns as one of the key challenges organizations face when implementing automation in PV.
As AI‑enabled workflows increasingly inform safety decisions, issues of accountability, transparency and patient protection continue to demand careful consideration.
Within the patchwork of global regulations, EU-based regulatory bodies place greater emphasis on data sovereignty and consent. The U.S. regulatory authorities, by comparison, prioritize validation rigor alongside ongoing oversight of system performance. For global pharmacovigilance teams, this difference in expectations complicates deployment decisions and operational consistency. When operations become complicated, compliance efforts can quickly become reactive as teams treat each new guideline as a standalone requirement.
For pharmacovigilance leaders, this reinforces the need for adaptable strategies that anticipate regulatory evolution rather than respond after the fact.
Shared Regulatory Principles as Strategic Anchors
While regional differences will continue to introduce complexity, regulatory guidance on AI in pharmacovigilance consistently converges around a core set of principles.
Clear objectives, fit-for-purpose models, robust data management and strong governance provide a common foundation for AI adoption, enabling pharmacovigilance organizations to align their strategies across regions and minimize regulatory friction. This alignment promotes global consistency while preserving the flexibility needed to meet local regulatory expectations.
The Expanding Role of AI Governance
Robust AI governance is foundational to adopting AI in pharmacovigilance. In this context, governance extends beyond technical transparency to encompass organizational accountability and meaningful human oversight. Clear ownership of AI systems ensures that responsibility is not diluted by automation, while well‑defined escalation and review mechanisms enable timely intervention when systems behave unexpectedly.
Establishing effective governance begins early in the AI lifecycle. Especially when patient safety outcomes are involved, teams should ask themselves, Is this use case appropriate for automation? Regulators increasingly look for models that are developed and validated with real-world data by leveraging data sets available within a company’s remit or perhaps by leveraging those available for purchase, rather than idealized scenarios, such as creating dummy data or records.
For pharmacovigilance teams that are translating regulatory principles into daily practice, several operational priorities consistently surface, such as:
- Regulatory and inspection readiness: Maintaining controlled documentation to support inspections and regulatory reviews, including SOPs, validation plans, risk‑based assessments (per FDA guidance) and an AI governance framework aligned with CIOMS XIV.
- AI control mechanisms: Implementing configurable thresholds that drive automated workflows supported by comprehensive audit trails with transparency on decisions taken.
- Continuous AI governance cycle: Regularly reviewing AI performance, evaluating the effectiveness of governance controls in identifying and addressing failures, and adapting models in response to evolving safety regulations.
Putting these practices into action helps bridge the gap between intent and operational reality. When done correctly, this approach can also develop and reinforce a culture where AI augments expert judgment instead of replacing it.
Transparency, Trust and Practical Compliance
Another critical factor in establishing trust with regulators, no matter the geography, is transparency. Regulators expect pharmacovigilance teams to clearly articulate how their AI-powered platforms support pre-signal detection, adverse event detection and case prioritization. This strengthens trust in both the technology and the process. Explainability does not require exposing proprietary algorithms, but it does demand clarity around inputs, outputs and limitations
Equally critical is the stewardship of data. While AI systems depend on large and diverse datasets, pharmacovigilance teams must demonstrate that use of data aligns with regional privacy and protection requirements. Embedding safeguards such as purpose limitation, access controls and data minimization not only mitigates compliance risk but also signals a commitment to responsible innovation that is increasingly central to regulatory trust.
Building Resilient AI Strategies for the Future
As adoption of AI in pharmacovigilance scales, so will its scrutiny from global regulators. Instead of waiting for complete regulatory certainty across different regions, teams should move forward by grounding their AI strategies in principles that already command broad consensus.
As AI continues to reshape pharmacovigilance, regulatory expectations will inevitably become more defined but not necessarily simpler. Organizations that ground their AI strategies in shared principles of governance, transparency and human oversight will be better equipped to adapt as global guidance evolves. In doing so, they move toward building systems that regulators trust, professionals rely on and patients ultimately benefit from.
Editor’s Note: Anuradha (Annie) Prabhakar is an Associate Director of Product Management for IQVIA’s Vigilance Detect product (safety risk identification technology). With over 20 years of professional experience, including more than a decade in pharmacovigilance (PV), Annie has a proven track record of managing large and complex projects for leading global pharmaceutical companies. Her expertise spans product management, PV remediation, automation and digital governance. She holds a master’s degree in Computer Applications from the University of Mysore. Her commitment to driving innovation and digital transformation in manual pharmacovigilance workflows has been a cornerstone of her career.