How AI Is Reshaping Pharmacovigilance: Key Takeaways From The New CIOMS Report

By Sumatha Kondabolu

Pharmacovigilance operations face mounting pressure from growing adverse event volumes and increasingly diverse safety data streams, including real-world evidence and social media. Integrating artificial intelligence into routine drug safety activities—such as case processing, medical coding, duplicate detection, and signal triage—offers a scalable path forward. However, sustainable adoption requires balancing operational automation with strict ethical controls and risk-proportionate oversight.

Establishing robust AI governance hinges on core principles outlined by international working groups like CIOMS. Maintaining meaningful human oversight, evaluating model performance across diverse population subgroups, and enforcing privacy-by-design standards safeguard system integrity. Furthermore, clear documentation, auditability, and transparent model performance metrics build regulatory trust while mitigating bias. Exploring these foundational principles helps drug safety teams deploy automated capabilities responsibly while maintaining compliance.