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?