Medical Imaging | December 2019

The rise of AI is one of the most popular topics in all of radiology, but there are still clear limits to its potential.

AI is expected to have a transformative impact on healthcare, especially radiology. According to a new analysis in the European Journal of Radiology, however, there are significant ethical issues that need to be addressed for any legitimate progress to be made.

Ensemble learning—the combination of multiple AI models into a single model with a single purpose—can lead to better overall results, according to new research published in Radiology: Artificial Intelligence.

AI can provide significant value to radiologists by sending urgent imaging studies to the top of their worklists, according to a new analysis published in Academic Radiology.

The American College of Radiology (ACR) Data Science Institute (DSI) has created a new resource for radiology researchers: a full list of FDA-cleared AI algorithms related to medical imaging.  

RSNA announced the winners of its third annual AI competition, the RSNA Intracranial Hemorrhage Detection and Classification Challenge, at RSNA 2019 in Chicago.

Deep learning-based prediction models can help healthcare providers diagnose small pulmonary nodules, according to a new study published in Academic Radiology.

Medical students see AI technologies as a considerable threat to the future of radiology, according to a new study published in the European Journal of Radiology.

AI-powered software can help radiologists identify malignant lung cancers with improved accuracy, according to new research published in Radiology.