Artificial Intelligence and Automation in Individual Case Safety Report (ICSR) Processing: Current Applications, Challenges & Future Directions

Abstract

Pharmacovigilance plays an important role in monitoring the safety of medicines, and Individual Case Safety Reports (ICSRs) provide essential information about suspected adverse events and adverse drug reactions. However, increasing reporting volumes, incomplete information, unstructured narratives, inconsistent terminology, and duplicate reports make ICSR processing challenging and time-consuming. This review discusses the current applications of artificial intelligence (AI) and automation in ICSR case processing, including the evolution from traditional manual methods and electronic reporting systems to rule-based automation, robotic process automation (RPA), natural language processing (NLP), machine learning (ML), and AI-assisted approaches. It explores their potential applications in case intake and validation, duplicate detection, data extraction, medical coding, case triage, medical review, narrative generation, quality control, and regulatory submission. These technologies may reduce repetitive manual work, improve processing efficiency, and support consistency in routine activities. However, their performance can be affected by poor data quality, incomplete clinical information, algorithmic bias, limited transparency, and difficulties in interpreting complex clinical situations. Appropriate system validation, continuous performance monitoring, traceability, regulatory compliance, and human oversight are therefore essential for reliable implementation. Overall, AI and automation offer promising opportunities to support ICSR processing, but their effectiveness depends on the quality of available data, the suitability of the technology, and appropriate quality-management practices. Future developments should focus on explainable AI, privacy-preserving approaches, improved interpretation of complex clinical information, and responsible integration into pharmacovigilance systems while retaining human expertise in safety-related decisions.