Possibility of using artificial intelligence models in routine pharmacovigilance processes

Abstract

Introduction  . Artificial intelligence (AI) has undergone rapid development in pharmacovigilance (PV), evolving from experimental application to being considered a key tool in daily practice. Relatively simple AI models, including statistical signal detection methods, have been used in PV for decades, while recent advances in Natural Language Processing (NLP) have significantly expanded the scope of potential applications. Objective  . This article provides a critical analysis of the potential of NLP systems to optimize routine PV tasks, taking into account data protection requirements. The application of semantic search AI models based on alternative architectural approaches, specifically embedding models and retrieval-augmented generation (RAG), is examined separately.  Main points  . The authors distinguish between processes that do not involve personal data and allow the use of open AI solutions (searching and systematizing scientific literature, generating publication summaries), and processes involving the handling of confidential information (e. g., data extraction from Individual Case Safety Reports (ICSRs), automated generation of clinical case descriptions, benefit-risk analysis, and compliance with regulatory requirements and reporting standards), which require the use of corporate AI systems deployed within a secure infrastructure. The article also discusses limitations, risks, practical implementation aspects, as well as issues of ensuring the reliability, reproducibility, and transparency of the solutions used. Conclusion  . AI models, particularly NLP models, have significant potential for integration into routine PV processes. However, successful integration of AI models is impossible without a systematic approach to managing associated risks. Subject to these conditions, AI can become a robust tool for increasing the efficiency of PV processes.