Medical Imaging

An AI algorithm helped determine adult women’s brains are, on average, a few years younger than the brains of males of the same age, according to research published in the Proceedings of the National Academy of Sciences.

A deep learning model classified acute and nonacute pediatric elbow abnormalities on radiographs in trauma with 88 percent accuracy, according to new research published in Radiology: Artificial Intelligence

AI for image analysis is predicted to be the top digital health technology for 2019 based on a survey of healthcare professionals, according to Forbes.

Russian researchers and radiologists have developed AI software that can distinguish and subsequently mark lung cancers on a CT scan within 20 seconds. 

Companies developing machine-learning solutions for medical imaging have received more than $1.2 billion in capital investments since 2014, according to a report by Signify Research.

The Radiological Society of North America (RSNA) has published the first issue of its new online journal focusing solely on AI in radiology.

Google’s AI tool for diagnosing diabetic retinopathy is having trouble reading images while it's being tested in an Indian hospital, according to a report by The Wall Street Journal.

Though AI continues to make great strides within radiology, some radiologists are still unprepared to educate medical students regarding its usage. This in turn may hinder medical students and trainees from pursuing radiology, according to a new editorial published in Academic Radiology.

Researchers have trained an AI system to interpret and prioritize chest X-rays based on urgent and critical findings, according to a study published in Radiology.

The radiology department at the Antwerp University Hospital in Belgium has incorporated an Aidoc tool that uses AI to help radiologists make faster diagnoses from CT scans, the university announced Wednesday, Jan. 16.

AI may be better at spotting cervical cancer and precancer after a study found a deep learning algorithm was more accurate at recognizing the disease than human doctors.

Seventy-seven percent of imaging professionals said machine learning is important, according to a recent report by Reaction Data. The findings signal an increase in use and understanding of machine learning among imaging professionals in the healthcare industry.