Artificial Intelligence

A combined deep learning method better detected hemorrhages and identified different subtypes of intracranial hemorrhage than single algorithms used alone, according to a new study published in the Journal of Digital Imaging.

There’s been plenty of hype around the potential of artificial intelligence (AI) to ease radiology workloads. And a new convolutional neural network approach detailed in a Jan. 22 study greatly reduced reporting backlog by accurately triaging chest x-rays in real-time.

The new evidence-based clinical recommendations seek to help labs better use quantitative image analysis (QIA) in HER2 testing for breast cancer.

According to a new market report from Reaction Data, 77 percent of medical imaging professionals believe that machine learning is “important," compared to 65 percent in 2017. Additionally, 59 percent of respondents reported they “understand” machine learning in 2018, compared to 52 percent in 2017.

A newly developed deep learning algorithm more accurately detected cervical precancer than highly experienced physicians and current testing methods, reported authors of a Jan. 10 study published in the Journal of the National Cancer Institute.

With data obtained from fMRI scans and machine learning, National University of Singapore-led (NUS) researchers have a better understanding of the cellular architecture of the brain.

A new artificial intelligence (AI) technology that can identify rare genetic diseases through analyzing an image of a patient's face could help cut diagnosis times for rare diseases and provide more personalized care, according to a new study published online Jan. 7 in Nature Medicine.

Using less than 1,000 imaging cases, researchers from Massachusetts General Hospital (MGH) in Boston were able to train an artificial intelligence (AI) algorithm to detect intracranial hemorrhage (ICH) and classify its five subtypes on unenhanced head CT scans, according to research published in the journal Nature Biomedical Engineering.

Follow-up recommendations in radiology reports commonly contain little standardization. Machine learning and deep learning methods are each effective for deciphering reports and may provide the foundation for real-time recommendation extraction, according to a recent study in the Journal of the American College of Radiology.

Researchers out of the University of Surrey in the U.K. created an artificial intelligence (AI) platform that can predict which cancer patients are most at risk for experiencing common symptoms associated with the disease.

Radiomic features extracted from CT images accurately distinguished small-cell lung cancer from benign nodules, according to a retrospective study published Dec. 18 in Radiology.

The desire to deliver patient-centered care drives many caring and high-achieving individuals to pursue a career in medicine, and AI can unburden today's physicians so they can stay focused on that primary goal.