Lung cancer is one of the malignant tumors with the highest incidence and mortality in the
world. The overall five-year survival rate of lung cancer is relatively lower than many leading …

Radiological reporting has generated large quantities of digital content within the electronic
health record, which is potentially a valuable source of information for improving clinical care …

Analyzing vast textual data and summarizing key information from electronic health records
imposes a substantial burden on how clinicians allocate their time. Although large language …

We systematically investigate lightweight strategies to adapt large language models (LLMs)
for the task of radiology report summarization (RRS). Specifically, we focus on domain …

The Impression section of a radiology report summarizes crucial radiology findings in natural
language and plays a central role in communicating these findings to physicians. However …

Automatically generating the “impression” section of a radiology report given the “findings”
section can summarize as much salient information of the “findings” section as possible, thus …

Automatically generating accurate summaries from clinical reports could save a clinician's
time, improve summary coverage, and reduce errors. We propose a sequence-to-sequence …

Study objective As use of radiology studies increases, there is a concurrent increase in
incidental findings (eg, lung nodules) for which the radiologist issues recommendations for …

Background It is essential for radiologists to communicate actionable findings to the referring
clinicians reliably. Natural language processing (NLP) has been shown to help identify free …

Sequence-to-sequence (seq2seq) network is a well-established model for text
summarization task. It can learn to produce readable content; however, it falls short in …