We present a radiology-specific multimodal model for the task for generating radiological
reports from chest X-rays (CXRs). Our work builds on the idea that large language model (s) …

In clinics, a radiology report is crucial for guiding a patient's treatment. However, writing
radiology reports is a heavy burden for radiologists. To this end, we present an automatic …

Automatically generated reports from medical images promise to improve the workflow of
radiologists. Existing methods consider an image-to-report modeling task by directly …

Conversational AI tools that can generate and discuss clinically correct radiology reports for
a given medical image have the potential to transform radiology. Such a human-in-the-loop …

The automatic generation of radiology reports given medical radiographs has significant
potential to operationally and improve clinical patient care. A number of prior works have …

Automated radiology report generation has the potential to reduce the time clinicians spend
manually reviewing radiographs and streamline clinical care. However, past work has …

Radiology report generation (RRG) aims to describe automatically a radiology image with
human-like language and could potentially support the work of radiologists, reducing the …

Multimodal models trained on large natural image-text pair datasets have exhibited
astounding abilities in generating high-quality images. Medical imaging data is …

Abstract We propose Retrieval Augmented Generation (RAG) as an approach for automated
radiology report writing, using multimodally-aligned embeddings from a contrastively …

With advances in deep learning and image captioning over the past few years, researchers
have recently begun applying computer vision methods to radiology report generation …