INTEGRATING ADVANCED ARTIFICIAL INTELLIGENCE TECHNIQUES IN ICSR PROCESSING: A COMPREHENSIVE FRAMEWORK FOR DUPLICATE DETECTION, TRIAGE, AND PREDICTIVE SIGNAL ANALYSIS

Abstract

Artificial Intelligence (AI) is transforming Individual Case Safety Report (ICSR) processing by addressing critical inefficiencies in pharmacovigilance systems. This paper explores AI-driven methodologies for duplicate detection, case triage, intake automation, risk prioritization, adverse event coding, causality assessment, and multilingual processing. Machine learning (ML) algorithms, including supervised and unsupervised models, enhance accuracy in structuring unstructured data, while natural language processing (NLP) extracts latent safety signals from case narratives. Despite advancements, challenges persist, such as algorithmic bias, model interpretability, and integration with legacy pharmacovigilance databases. This study evaluates convolutional neural networks (CNNs), transformer architectures, and federated learning frameworks for ICSR automation. Results indicate a 40–60% reduction in human error and a 30% improvement in high-risk case identification. However, regulatory compliance, data quality heterogeneity, and cross-lingual semantic ambiguity remain unresolved. Future research must prioritize explainable AI (XAI) and real-time predictive analytics to optimize ICSR workflows.