Artificial Intelligence in Pharmacovigilance: Opportunities, Challenges and the Emerging Role of Clinical Pharmacists

Artificial Intelligence (AI) has emerged as a transformative technology reshaping pharmacovigilance (PV)-the science and activities relating to the detection, assessment, understanding, and prevention of Adverse Drug Reactions (ADRs). Traditional PV systems, which rely heavily on manual data entry, coding, and case assessment, are increasingly challenged by the rapid growth of real-world data sources such as Electronic Health Records (EHRs), literature databases, social media, and spontaneous reporting systems. AI tools such as Machine Learning (ML), deep learning, and Natural Language Processing (NLP) can analyse both structured and unstructured datasets, enabling faster signal detection and improved predictive analytics. These innovations enhance the efficiency, accuracy, and scalability of PV processes. However, challenges persist, including data quality issues, algorithmic bias, privacy concerns, and the need for human oversight. The integration of AI with clinical expertise-especially the role of clinical pharmacists-can transform pharmacovigilance from a reactive to a proactive system, ultimately improving patient safety and global drug surveillance.