Abstract Machine-learning models for medical tasks can match or surpass the performance
of clinical experts. However, in settings differing from those of the training dataset, the …

Multi-modal data abounds in biomedicine, such as radiology images and reports.
Interpreting this data at scale is essential for improving clinical care and accelerating clinical …

Background Recent advances in performance of natural language processing (NLP)
techniques have spurred wider use and more sophisticated applications of NLP in radiology …

Please see the Editorial Comment by Hillary W. Garner discussing this article. Fractures are
common injuries that can be difficult to diagnose, with missed fractures accounting for most …

Recent progress in Medical Artificial Intelligence (AI) has delivered systems that can reach
clinical expert level performance. However, such systems tend to demonstrate sub-optimal" …

We propose a new strategy for applying large pre-trained language models to novel tasks
when labeled training data is limited. Rather than apply the model in a typical zero-shot or …

Purpose To compare the effectiveness of weak supervision (ie, with examination-level labels
only) and strong supervision (ie, with image-level labels) in training deep learning models …

The Radiological Society of North of America (RSNA) and the Medical Image Computing
and Computer Assisted Intervention (MICCAI) Society have led a series of joint panels and …

Human tissue, which is inherently three-dimensional (3D), is traditionally examined through
standard-of-care histopathology as limited two-dimensional (2D) cross-sections that can …

Recently multimodal medical pretraining models play a significant role in automatic medical
image and text analysis that has wide social and economical impact in healthcare. Despite …