Artificial Intelligence

There is an "immaturity" between machines and humans, said Paul J. Chang, MD, of the University of Chicago. Unless radiology institutions augment their current IT infrastructure, AI could become another technological-driver of burnout.

For AI to become clinically feasible in women’s imaging, it must excel in the areas of performance, time, workflow and cost, according to an opinion piece published online Feb. 19 in the American Journal of Roentgenology.  

The software, which analyzes tumors on CT scans, was up to four-times more accurate at predicting ovarian cancer deaths compared to standard methods, according to research published Feb. 15 in Nature Communications.

The new platform, affectionately called ‘Herman,' analyzes complex patterns in images of pathogen and human cell interactions, and can do so in a fraction of the time normally required.

A multi-institutional team of researchers has developed a new AI learning algorithm that can distinguish between low- and high-risk prostate cancer from multiparametric MRI (mpMRI) scans with higher sensitivity and predictive value than current risk assessment approaches, according to research published online Feb. 7 in the journal Scientific Reports.

In a special report published Jan. 30 in the inaugural issue of Radiology: Artificial Intelligence, Luciano M. Prevedello, MD, and colleagues recognize the challenges of implementing AI into clinical workflows, but also offer potential solutions—specifically image-based competitions—which could foster faster, more collaborative AI research.

The Massachusetts Institute of Technology (MIT)’s Laboratory for Computational Physiology has published their MIMIC-Chest X-Ray Database (MIMIC-CXR)—a collection of more than 350,000 chest x-rays associated with 227,943 imaging studies sourced from Beth Israel Deaconess Medical Center in Boston.

The system, named Doctor Alzimov, after the Russian-born science fiction writer, can be installed on any computer and can find nodules as small as 2 millimeters.

In 2018, capital investments in startup companies developing medical imaging AI solutions reached almost $580 million—more than double the 2017 amount of $270 million, according to a new market report published Jan. 31 by Signify Research.

A 14-layer convolutional neural network (CNN) trained on MRI and pathology data accurately predicted the molecular subtype of breast cancers, according to a Jan. 31 study published in the Journal of Digital Imaging. The method may help personalize treatment plans for the disease.

The world market for digital pathology is expected to rise to $600 million by 2022, according to a new report from Signify Research. But it will have to overcome some strong obstacles first.

A team from Singapore demonstrated that an object detection convolutional neural network (CNN) could accurately detect and localize fractures on wrist x-rays, according to a Jan. 30 study published in Radiology: Artificial Intelligence. The method may be more verifiable than traditional CNNs.