Review of Natural Language Processing in Pharmacology

Abstract

Natural language processing (NLP) is an area of artificial intelligence that applies information technologies to process the human language, understand it to a certain degree, and use it in various applications. This area has rapidly developed in the last few years and now employs modern variants of deep neural networks to extract relevant patterns from large text corpora. The main objective of this work is to survey the recent use of NLP in the field of pharmacology. As our work shows, NLP is a highly relevant information extraction and processing approach for pharmacology. It has been used extensively, from intelligent searches through thousands of medical documents to finding traces of adversarial drug interactions in social media. We split our coverage into five categories to survey modern NLP methodology, commonly addressed tasks, relevant textual data, knowledge bases, and useful programming libraries. We split each of the five categories into appropriate subcategories, describe their main properties and ideas, and summarize them in a tabular form. The resulting survey presents a comprehensive overview of the area, useful to practitioners and interested observers. Significance StatementThe main objective of this work is to survey the recent use of NLP in the field of pharmacology, in order to provide a comprehensive overview of the current state in the area after the rapid developments which occurred in the last few years. We believe the resulting survey to be useful to practitioners and interested observers in the domain.

Skin and Syntax: Large Language Models in Dermatopathology

“…NLP works by utilizing complex algorithms and linguistic models to decipher the intricacies of human language. It enables computers to not only recognize words but also comprehend context, syntax, semantics, and subtle nuances, which may not be picked up by human observers, that are inherent in communication [5]. This technology has revolutionized medical practices by enabling the extraction of vital information from unstructured texts, such as clinical notes and research papers.…”