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

Machine learning has long been used at UMC. In fact, our vigiGroup method stems from neural networks pioneered by one of this year’s Nobel laureates in Physics.

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?