Leveraging Artificial Intelligence in Pharmacovigilance: Enhancing Drug Safety Through Data-Driven Approaches

Pharmacovigilance (PV) plays a crucial role in ensuring drug safety by monitoring adverse drug reactions (ADRs) and assessing the risk-benefit profiles of pharmaceutical products. Traditional PV methods rely heavily on manual reporting and analysis, which are often time-consuming, prone to human error, and limited in scalability. The integration of Artificial Intelligence (AI), including machine learning (ML) and natural language processing (NLP), has the potential to transform PV by automating case intake, improving signal detection, and enabling real-time safety monitoring. AI-driven models can efficiently analyze large datasets from electronic health records (EHRs), regulatory databases, and social media platforms to identify potential safety concerns. Despite its advantages, AI in PV faces challenges such as data privacy concerns, regulatory compliance, and algorithmic bias. Ensuring transparency, explainability, and adherence to global regulatory frameworks is critical for the successful implementation of AI-driven drug safety monitoring. This paper explores the applications of AI in PV, evaluates its benefits and limitations, and discusses future directions for AI-driven pharmacovigilance.