In this study, we uncover the unexpected efficacy of residual-based large language models
(LLMs) as part of encoders for biomedical imaging tasks, a domain traditionally devoid of …

In this paper, we aimed to provide a review and tutorial for researchers in the field of medical
imaging using language models to improve their tasks at hand. We began by providing an …

In this preliminary work, we present a domain fine-tuned LLM model for radiology, an
experimental large language model adapted for radiology. This model, created through an …

This paper reveals that large language models (LLMs), despite being trained solely on
textual data, are surprisingly strong encoders for purely visual tasks in the absence of …

Multimodal large language models (MLLMs) like LLaVA and GPT-4 (V) enable general-
purpose conversations about images with the language modality. As off-the-shelf MLLMs …

Abstract Large Language Models (LLMs) have consistently showcased remarkable
generalization capa-bilities when applied to various language tasks. Nonetheless …

Methods A comprehensive literature search was conducted on the Web of Science and
PubMed databases from 2013 to 2023, focusing on the transformations of LLMs in Medical …

In the realms of computer vision and natural language processing, Large Vision-Language
Models (LVLMs) have become indispensable tools, proficient in generating textual …

Large language models (LLMs) have revolutionized the global landscape of technology
beyond natural language processing. Owing to their extensive pre-training on vast datasets …

Language-supervised pre-training has proven to be a valuable method for extracting
semantically meaningful features from images, serving as a foundational element in …