ARTIFICIAL INTELLIGENCE (AI) IN PHARMACOVIGILANCE FOR EARLY DETECTION OF ADVERSE DRUG REACTIONS

Abstract

Pharmacovigilance plays a crucial role in monitoring drug safety after market approval, particularly in the context of increasing volume and complexity of healthcare data. This retrospective observational study aimed to evaluate the application of artificial intelligence (AI) techniques in the analysis of textual data from secondary pharmacovigilance sources, focusing on the identification and organization of potential adverse drug reactions. Unstructured textual records, including spontaneous reports, clinical narratives, and technical documents, were analyzed using systematic preprocessing, text mining, and natural language processing techniques. Machine learning models were applied for text classification, clustering, and detection of recurring patterns within the data. Model performance was assessed using standard statistical metrics such as accuracy, precision, sensitivity, and cross-validation procedures. The results demonstrated that AI-based approaches improved the efficiency of initial data screening and semantic standardization, facilitating the identification of potential safety signals. Despite reliance on data quality and the need for expert validation, the findings suggest that AI can complement traditional pharmacovigilance methods. In conclusion, artificial intelligence shows potential to enhance drug safety monitoring systems when integrated with established clinical and regulatory practices, while acknowledging the inherent limitations of retrospective observational designs.