Artificial Intelligence and Machine Learning in Pharmacovigilance

Pharmacovigilance (PV) is the systematic monitoring of medicines for the detection, evaluation and prevention of harmful drug reactions (ADRs) aftermarket provision. Traditional PVs are based on spontaneous reporting and manual signal detection, and can be expensive and often slow. In recent years, artificial intelligence (AI) and Machine Learning (ML) methods have shown promising PVs by evaluating large and diverse data sources (clinical records, social media, scientific literature, etc.) much faster than humans. The AI/ML methods (including natural language processing (NLP) and deep learning) were successfully applied to the task as automated ADR detection, case report processing, and literary exams. These methods improve the sensitivity and locality of CAC signal detection, allowing personalized risk stratification in real-time security monitoring. However, AI-controlled PVs also face challenges related to data quality, interpretability and regulatory oversight. Current regulatory initiatives (e.g. EMAS AI Reflection Paper, Draft FDA Guidelines) highlight the need for AI tools to comply with the benefits, limitations and forward-looking statements of legal and ethical standards.