Literature Management and the Impact of AI in Drug Safety

When a pharmaceutical company markets a drug, the safety monitoring journey is just beginning. Every week, pharmacovigilance(PV) teams must screen thousands of scientific publications from databases like PubMed and EMBASE, searching for adverse drug reactions that could signal emerging safety concerns. 

The problem is that even if the PV team misses finding one critical case report, it can put patients at risk. On the flip side, when the same case appears across multiple journals and gets counted twice, regulatory authorities might see false signals that trigger unnecessary investigations.

At this point, literature monitoring is becoming both a regulatory mandate and a significant operational challenge. However, artificial intelligence is changing how drug safety teams handle this workload. In this article, we will look at the specific challenges PV teams face in literature monitoring, various approaches to solve these problems, and how AI-powered solutions are improving
outcomes.

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