COMPUTATIONAL LINGUISTIC TECHNIQUES IN THE ORGANIZATION, HARMONIZATION, AND STANDARDIZATION OF MODERN PHARMACEUTICAL TERMINOLOGY

Abstract

This research explores the application of computational linguistic techniques in the organization, harmonization, and standardization of modern pharmaceutical terminology. With the continuous emergence of new drugs, treatment methods, and biomedical innovations, the linguistic landscape of pharmacology has become increasingly complex and multilingual. The study emphasizes the use of artificial intelligence, natural language processing (NLP), and semantic modeling to systematically analyze pharmaceutical texts, extract relevant terms, and unify their usage across different languages and regulatory frameworks. By integrating terminology with international standards such as WHO INN, ATC, and MedDRA, the approach ensures clarity, reduces ambiguity, and enhances interoperability in global healthcare systems. Additionally, dynamic computational models facilitate real-time updates of emerging terms, enabling accurate communication between researchers, clinicians, and regulatory agencies. The findings highlight that computer-based linguistic modeling not only strengthens the precision and consistency of pharmaceutical terminology but also contributes to improved patient safety, cross-border collaboration, and efficient digital healthcare management.