Artificial Intelligence for Next-Gen Pharmacovigilance and Post Marketing Surveillance in Evolving the Healthcare Environment

Abstract

In the rapidly evolving healthcare environment, pharmacovigilance (PV), the science of detecting, assessing, and preventing adverse drug reactions (ADRs) remains an essential part of clinical practice and drug safety governance worldwide. Traditional PV systems rely on voluntary reporting and non-automated data processing, thus leads to low reporting rates, slow signal detection, and poor analytical performance, and fail to keep pace with the growing number of drugs and the volume of data. Post-marketing surveillance (PMS), a cornerstone of PV systems, faces particular strain as the sheer scale of real-world drug exposure data generated after regulatory approval increasingly overwhelms conventional monitoring infrastructure. AI technologies have demonstrated transformative potential across principal PV functions: Natural Language Processing (NLP)-driven pipelines extract ADR signals from electronic health records (EHRs) and scientific literature with enhanced sensitivity, while Machine Learning (ML) classifiers improve signal detection specificity within spontaneous reporting databases such as FDA Adverse Event Reporting System (FAERS) and VigiBase, offering transformative potential for strengthening PMS programs globally. Ethical considerations—including algorithmic bias (prejudice in training data), model opacity, and hallucination risks in generative systems—demand rigorous attention and auditing. With its enormous potential, AI can revolutionise PV from a reactive, resource-heavy field into a proactive, precision-driven safety science reshaping post-marketing surveillance from a passive data collection exercise into a dynamic, real-time risk management system requiring multistakeholder collaboration among industry, regulators, clinicians, and informaticists, supported by validated, transparent, and ethically governed systems.