Artificial Intelligence

The technique combines AI with patient-specific health and cost information for a rough estimate on an individual's five-year healthcare expenditures.

The challenge tasked teams with developing an algorithm capable of identifying and classifying subtypes of hemorrhages on head CT scans.

Massachusetts Institute of Technology researchers harnessed machine learning to create conditional atlases that can help clinicians diagnose a wider subset of patients. 

The models encompass a wide variety of diagnostic tasks, including pneumothorax detection on chest x-rays and highlighting brain segments on MRI scans.

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.