Experts have long talked about an ideal future in which radiologists work alongside AI. A new platform may have the answer, combining the intelligence of man and machine to better diagnose pneumonia.

In an exclusive conversation with HealthImaging, John D. Banja, a professor of medical ethics at Emory University, discusses plans to launch a series of audio chats with radiologists, exploring one of the profession's stickiest issues.

In fact, clinicians who took a second look at x-rays using the deep learning software improved their sensitivity, on average, by 5.2%.

The novel method uses a deep neural network to improve fluorescence lifetime imaging, which allowed scientists at Rensselaer Polytechnic Institute to view molecular-level interactions within cells.

Coronary artery calcium scoring has proven to be more predictive of cardiovascular risk than any other biomarker, but quantifying scores via imaging remains a time-consuming and labor-intensive task.

Radiologists, medical students and surgeons all agree that AI should be incorporated into diagnostic radiology, but for the most part their perceptions of the technology are drastically different.

Radiologists from the Netherlands believe deep learning can significantly impact cardiac MRI analysis in the not so distant future, sharing their thoughts in a piece published in the American Journal of Roentgenology.

Deep learning can identify cancerous and precancerous esophagus tissue on digitized pathology slides, opening the door for AI to alter the digital pathology landscape.

A new AI algorithm developed by researchers at Case Western Reserve University can predict which malignant breast cancers will progress and benefit from additional treatment.

“Our broad radiology community has a unique opportunity to engage with this exciting scientific evolution, working with interdisciplinary ‘convergence science’ teams,” Andrea Rockall, clinical chair of radiology, Imperial College London, wrote in a new editorial.

Facebook and NYU Langone Health announced their commitment to making MRI scans more efficient through the use of AI more than one year ago, and a new story from Popular Science provides a glimpse into what the two have been up to.

“Machine learning is a low-cost and efficient tool that could help physicians arrive to a quicker decision as to how to approach an indeterminate nodule,” lead author of the study, John Eisenbrey, PhD, said.