Background Prompt engineering, focusing on crafting effective prompts to large language
models (LLMs), has garnered attention for its capabilities at harnessing the potential of …

Objective To improve performance of medical entity normalization across many languages,
especially when fewer language resources are available compared to English. Materials …

Recent advancements in neural machine translation (NMT) offer promising potential for
generating cross-language clinical natural language processing (NLP) resources. There is a …

Background: Clinical named entity recognition (CNER) is a fundamental task in natural
language processing used to extract named entities from electronic medical record texts. In …

Delays in translating new medical evidence into clinical practice hinder patient access to the
best available treatments. Our data reveals an average delay of nine years from the initiation …

Objectives: To introduce the 2023 International Medical Informatics Association (IMIA)
Yearbook by the editors. Methods: The editorial provides an introduction and overview to the …

The advent of large language models (LLMs) such as BERT and, more recently, GPT, is
transforming our approach of analyzing and understanding biomedical texts. To stay …

Recent advancements in neural machine translation (NMT) offer promising potential for
generating crosslanguage clinical natural language processing (NLP) resources. There is a …

The advent of large language models (LLMs) such as BERT and, more recently, GPT, is
transforming our approach of analyzing and understanding biomedical texts. To stay …

Abstract Named Entity Recognition (NER) models based on Transformers have gained
prominence for their impressive performance in various languages and domains. This work …