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 …

Medical vision-language models (VLMs) combine computer vision (CV) and natural
language processing (NLP) to analyze visual and textual medical data. Our paper reviews …

This paper introduces MedMCQA, a new large-scale, Multiple-Choice Question Answering
(MCQA) dataset designed to address real-world medical entrance exam questions. More …

Open domain question answering (OpenQA) tasks have been recently attracting more and
more attention from the natural language processing (NLP) community. In this work, we …

Recent advancements in large language models (LLMs) have transformed the field of
question answering (QA). However, evaluating LLMs in the medical field is challenging due …

Large language models (LLMs) have shown promise for generative and knowledge-
intensive tasks including question-answering (QA) tasks. However, the practical deployment …

Privacy and ethical considerations limit access to large-scale clinical datasets, particularly
clinical text data, which contain extensive and diverse information and serve as the …

Automatic Question Answering (QA) has been successfully applied in various domains such
as search engines and chatbots. Biomedical QA (BQA), as an emerging QA task, enables …

Neural image-to-text radiology report generation systems offer the potential to improve
radiology reporting by reducing the repetitive process of report drafting and identifying …

Automatic generation of clinical notes from doctor-patient conversations can play a key role
in reducing daily doctors' workload and improving their interactions with the patients …