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

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.

Researchers from the Massachusetts General Hospital (MGH) in Boston designed a deep-learning algorithm that provides the reasoning behind its decisions, which could help solve transparency issues associated with AI, according to a report by Health Imaging.

An AI-driven approach for detecting an early sign of diabetic retinopathy achieved an accuracy rate of more than 98 percent, according to a study published in Computers in Biology and Medicine. The results could mean a quicker and cheaper solution for diagnosing the disease earlier and possibly prevent loss of eyesight.