Abstract

The process of developing treatments and making them available to the public can be laborious and time consuming. To ensure the safety of products, pharmaceutical, biotech and medtech companies conduct clinical studies that evaluate the safety and efficacy of their offerings. While these studies can involve many participants, the number of subjects is smaller compared to the number of real-world consumers.

This is where pharmacovigilance plays a critical role. Clinical and regulatory documentation has long been in a stagnant phase; efforts to create and manage documents can be not only prolonged, but also incredibly arduous. Professionals, such as medical writers, regulatory affairs specialists and individuals involved in pharmacovigilance are increasingly overwhelmed with the number of reports and the processes involved in detailing adverse events which can also be plagued with errors and inefficiencies.

However, this is rapidly changing for the better. The potential of generative artificial intelligence (AI) in streamlining the processes involved in pharmacovigilance has been tested and the results are promising. This article explores how generative AI can optimise pharmacovigilance workflows, reduce reporting burdens and improve accuracy and timeliness across the pharmaceutical lifecycle.