V3I10P1

Artificial Intelligence in Pharmacovigilence: Transforming Adverse Drug Reaction Detection, Signal Detection and Drug Safety Monitoring

Nikath Sulthana^1*

Abstract

Pharmacovigilance is an essential component of the medicine life cycle, concerned with the detection, assessment, understanding and prevention of adverse effects and other medicine-related problems. Conventional pharmacovigilance systems depend on spontaneous adverse drug reaction reporting, individual case safety reports, clinical databases, electronic health records, epidemiological studies and regulatory assessment. Although these systems have contributed substantially to medicine safety, the increasing volume, heterogeneity and complexity of health data have created significant challenges for timely and comprehensive safety monitoring. Artificial intelligence (AI), particularly machine learning (ML), deep learning, natural language processing (NLP) and large language models, has emerged as a potential means of augmenting pharmacovigilance activities.AI-based systems can process structured and unstructured data at a scale that is difficult to achieve through entirely manual workflows. Applications include adverse drug reaction and adverse drug event detection, automated case intake and triage, clinical-text mining, social-media surveillance, duplicate detection, case prioritization, safety signal detection, real-world data analysis and support for regulatory safety monitoring. Machine learning approaches can identify complex patterns in large datasets, while NLP techniques can extract drug-event relationships from clinical narratives and patient-generated content. More recent transformer-based models and large language models provide additional capabilities for contextual interpretation and information extraction, although their use introduces concerns regarding hallucination, reproducibility, transparency and validation.Despite substantial progress, AI should not be regarded as an autonomous replacement for pharmacovigilance professionals. Data incompleteness, reporting bias, confounding, class imbalance, poor generalizability, algorithmic bias, privacy concerns and limited interpretability can affect the reliability of AI-generated outputs. Governance, validation, human oversight and continuous performance monitoring are therefore essential. Recent regulatory initiatives, including the joint FDA and European Medicines Agency guiding principles for good AI practice in drug development, emphasize human-centered design, risk-based assessment, data governance, multidisciplinary expertise and life-cycle management.This review examines the role of AI throughout the pharmacovigilance process, from adverse-event identification and case processing to signal detection and real-world safety surveillance. It also discusses machine learning, NLP, deep learning, large language models, explainable AI, governance, regulatory considerations, limitations and future directions. A human-centered model in which AI augments rather than replaces pharmacovigilance expertise may provide a practical framework for responsible implementation.

Keywords:

Artificial intelligence; pharmacovigilance; machine learning; adverse drug reactions; natural language processing; safety signal detection; real-world data; large language models.