Artificial Intelligence

iCAD announced Thursday, July 11, that its ProFound AI solution for 2D mammography has gained CE mark approval.

Deep learning algorithms can manage thyroid nodules on ultrasound (US) images at a level comparable to expert radiologists, according to new research published in Radiology.

The algorithm improved the specificity of thyroid biopsy recommendations, beating seven of nine radiologists. With more research, the algorithm could help in the decision-making process for assessing thyroid nodules.

While AI wasn’t the only topic discussed during the SIIM 2019 annual meeting, every issue seemed to be tied to the emerging technology in one way or another.

Data security has become a serious issue in the U.S., not only for big tech companies like Facebook, but for vendors and institutions looking to use patient imaging information to develop AI platforms.

The American College of Radiology (ACR) has expanded its ACR AI-LAB pilot program geared toward helping radiologists develop AI models without the use of coding language.

A convolutional neural network (CNN) approach can accurately identify and sub-classify suspected tuberculosis (TB) on chest radiographs, according to research presented at the Society for Imaging Informatics in Medicine (SIIM) annual meeting.

In order to properly train and validate algorithms, developers need high volumes of quality-labeled data. But such datasets are not easy to obtain.

Predictions of heart attacks and deaths based on coronary computed tomography angiography (CCTA) are more accurate when made using an artificial intelligence (AI) algorithm than with the Coronary Artery Disease Reporting and Data System (CAD-RADS) or other risk assessment methods.

Machine learning (ML) can help healthcare providers predict heart disease—including heart attacks—better than other popular risk models, according to new research published in Radiology.

As AI technologies continue to evolve, they may be able to make a significant impact on patient care by reducing the amount of time physicians spend sorting through paperwork and documentation.

Machine learning (ML) can help providers extract all relevant facts from radiology reports in real time, according to a new study published in the Journal of Digital Imaging.