Automation and machine learning promise to revolutionise the way we process medicines safety information, but we must be well prepared before embracing the change.

Catch up on the latest research exploring how increasingly complex machine learning models pose a validation challenge for the pharmacovigilance community.

Deep learning shows promise removing personal identifiers from case reports, according to new work presented by UMC researchers.

A new study highlights the gains to be made in time, quality, and consistency from using UMC’s coding engine WHODrug Koda to automate drug coding in post-marketing surveillance.

Ensuring trust in AI is vital to fully reap the benefits of the technology in pharmacovigilance. Yet, how do we do so while grappling with its ever-growing complexity?

Duplicate reports are a big problem when it comes to signal detection, but with the help of machine learning and new ways of comparing reports, we may more effectively detect them.

AI holds potential to transform pharmacovigilance. But navigating the risks requires critical assessment. This article explores AI's evolving role in medicines safety.