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?

As the influence of artificial intelligence continues to grow, researchers are finding more and more new ways to take advantage of convolutional neural networks (CNNs) in healthcare. According to a new study published in Radiology, using a CNN as a deep learning algorithm can help improve the overall quality of arterial spin labeling (ASL) image quality.

Researchers have created a new algorithm that uses brain scans to predict language ability in deaf children after they receive a cochlear implant, according to a study published in the Proceedings of the National Academy of Sciences.

The two-day AI in Healthcare Summit on Thursday and Friday, Jan. 18 and 19, at the Harvard Club in Boston offers an in-depth discussion on the current state of the healthcare AI industry, AI-driven advancements in medical imaging and diagnostics, surgical robotics, patient engagement, integration and interoperability opportunities and challenges, bringing the human aspect back to healthcare, legal considerations and AI’s role in value-based care.

vRad, a MEDNAX company, announced this week that it has secured a patent for using artificial intelligence (AI) to escalate high-priority radiology procedures.

Machine learning using deep convolutional neural networks (CNNs) can be used to detect fractures in plain radiographs, according to a new study published in Clinical Radiology.