The clinical use of MRI in patients with multiple sclerosis (MS) has advanced markedly over
the past few years. Technical improvements and continuously emerging data from clinical …

OBJECTIVE. In this article, we describe some of the cognitive and system-based sources of
detection and interpretation errors in diagnostic radiology and discuss potential approaches …

Imaging research laboratories are rapidly creating machine learning systems that achieve
expert human performance using open-source methods and tools. These artificial …

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 …

Sifting through vast textual data and summarizing key information from electronic health
records (EHR) imposes a substantial burden on how clinicians allocate their time. Although …

Recently a number of studies demonstrated impressive performance on diverse vision-
language multi-modal tasks such as image captioning and visual question answering by …

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 …

Neural abstractive summarization models are able to generate summaries which have high
overlap with human references. However, existing models are not optimized for factual …

Much attention has been given to machine learning and its perceived impact in radiology,
particularly in light of recent success with image classification in international competitions …

Natural Language Generation (NLG) has made great progress in recent years due to the
development of deep learning techniques such as pre-trained language models. This …