Role of Artificial Intelligence in Pharmacovigilance

Pharmacovigilance ensures the safety and efficacy of any pharmaceutical drug products or medical devices throughout their lifecycle. Traditional methods available for monitoring of adverse drug reactions (ADRs) are resource limited and prone to cause underreporting of ADRs. Recent technological advancement in pharmacovigilance reporting is the integration of artificial intelligence (AI) and ADR reporting. In this chapter we investigated the role of AI technologies in pharmacovigilance, emphasizing its potential benefits. AI technologies cover machine learning, deep learning, and natural learning processing (NLP) that have shown remarkable ability in automation of pharmacovigilance data like signal detection, risk assessment, and regulatory compliance. AI algorithms help in analyzing the extensive amounts of unstructured data from different sources like electronic health records (EHRs), medical literature and identification of real-time ADR data. In addition, AI models help in facilitating early detection of ADRs and give valuable insights in identifying risk–benefit profile of drug. Although AI technologies have several advantages, there are also some challenges related to the quality of data, transparency, bias, and regulatory compliance. All these challenges need to be addressed to entirely use the potential of AI technology in pharmacovigilance. Combined efforts are required between AI technologies, regulatory agencies, and pharmaceutical companies to establish the standard guidelines and safeguard the ethical and responsible use of AI technology in pharmacovigilance. There is a need to refine the AI-driven pharmacovigilance strategies by developing healthcare and drug safety decision-making, ultimately benefiting the patients.