The worlds of radiology and artificial intelligence (AI) are at a bit of a crossroads, according to a new analysis published in the Journal of the American College of Radiology. While experts from both fields remain focused on making technological breakthroughs, the actual relationship between radiology and AI is not getting the attention it deserves.

The National Evaluation System for Health Technology (NEST) Coordinating Center announced Feb. 2 it had selected a use case from the American College of Radiology (ACR) Data Science Institute (DSI) as one of its first demonstration projects.

Machine learning models can identify key information in radiology reports with significant accuracy, according to a new study published in Radiology.

Research firm Reaction Data has published a new report, “Machine Learning in Medical Imaging,” that breaks down what radiologists and other imaging professionals think about AI, machine learning and the future of radiology.

The Dubai Health Authority (DHA) is using artificial intelligence (AI) to sort chest x-rays that detect tuberculosis, a test which is a mandatory requirement for all expats desiring a visa to stay in the UAE.

For artificial intelligence (AI) to develop within healthcare, accessibility to “high-quality data” is crucial, according to a report commissioned by the Office of the National Coordinator for Health IT (ONC) and the Agency for Healthcare Research and Quality (AHRQ).

Interpreting free-text radiology reports can be a challenge for machine learning, according to a new article published in the Journal of the American College of Radiology.

As much as the relationship between artificial intelligence (AI) and radiology has already developed, it is still in its earliest stages. What will that relationship look like in a decade? Or in another 20 or 30 years?