<p>Pharmacovigilance (PV) is essential for the surveillance of drug safety; however, existing forms of PV are based on passive&ensp;reporting systems. Problems of under-reporting, data volume, manual processing bottlenecks and delayed signal detection prevent timely identification of&ensp;Adverse Drug Reactions (ADRs). The automation of ADR&ensp;detection are in various ways in which Artificial Intelligence (AI), and specifically Machine Learning (ML) and Natural Language Processing (NLP) can potentially transform the area. Artificial intelligence (AI) also boosts the efficiency, agility, and sensitivity of pharmacovigilance activities&ensp;by leveraging real-world data sources such as Electronic Health Records (EHRs), academic publications, or social media. The benefits include quicker case treatment, early signal identification, and new adverse&ensp;drug reaction detection. Certainly, there are data quality issues to address, interpretation (&quot;black box&quot;), how it&ensp;can be integrated into workflows that already exist and the negation of biases in algorithms, which should still be something that is tested. The application of AI to pharmacovigilance has the potential to transform it from a reactive and passive, into a predictive, proactive, robust and efficient tool benefiting patient safety&ensp;through early intervention and more comprehensive safety surveillance.</p>