Artificial intelligence (AI) is transforming pharmacovigilance. From automated case processing to signal detection and duplicate identification, AI systems promise to enhance efficiency, improve quality, and enable entirely new capabilities. However, the rapid proliferation of AI tools—coupled with decreasing development costs—creates a critical challenge: how do we know which AI systems actually work?
With over 6,000 identified rare diseases and millions of medical devices in daily use worldwide, standardized adverse event reporting is critical for public health safety.
Version 29.0, released in March 2026, introduces significant terminology updates, including new terms, revised hierarchies, changes to Standardised MedDRA Queries (SMQs), and important modifications to medication error concepts.
By integrating Directed Acyclic Graphs (DAGs) from causal inference into pharmacovigilance practice, the authors provide a structured way to map, understand, and mitigate the biases that have long plagued signal detection.
This study investigated why reports of side effects (adverse drug reactions or ADRs) from a common HIV medication in Uganda are often incomplete, which hinders effective drug safety monitoring.
This is a fundamental concept in pharmacovigilance (drug safety monitoring). Let's break down the Important Medical Event (IME) List from MedDRA with a clear illustration.
This document is an official guidance from the U.S. Food and Drug Administration (FDA) issued in December 2025. It provides the agency's current recommendations and interpretations on how clinical investigators (the doctors/researchers running clinical trials) should handle safety reporting for drugs and medical devices that are still under investigation.
Advancing Medication Safety Through Knowledge and Vigilance
