Large Language Models (LLMs) went from non-existent to ubiquitous in the machine
learning discourse within a few years. Due to the fast pace of the field, it is difficult to identify …

Pre-trained language models (PLMs) have been the de facto paradigm for most natural
language processing tasks. This also benefits the biomedical domain: researchers from …

Artificial intelligence (AI), especially the most recent large language models (LLMs), holds
great promise in healthcare and medicine, with applications spanning from biological …

GPT-4 demonstrates high accuracy in medical QA tasks, leading with an accuracy of
86.70%, followed by Med-PaLM 2 at 86.50%. However, around 14% of errors remain …

Given the overwhelming and rapidly increasing volumes of the published biomedical
literature, automatic biomedical text summarization has long been a highly important task …

Abstract Clinical Natural Language Processing has been an increasingly popular research
area in the NLP community. With the rise of large language models (LLMs) and their …

Abstract Automatically Impression Generation (AIG) can conclude essential information of
the “Findings” section, thus facilitating more effective communication between radiographers …

Abstractive text summarization has emerged as a promising approach for generating
concise and informative summaries from radiology reports. The topic of research focuses on …

Background: Large Language Models (LLMs) offer users natural language interaction,
technical insights and task automation capabilities. However, the systematic integration of …