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.

Imaging groups throughout the United States have moved to standardized radiology reports in recent years, and it’s a trend that continues to pick up steam. One side effect of this change is that leaders must then perform long, labor-intensive manual audits of their team’s reports to confirm compliance. But what if groups could somehow perform an automated audit, making those pesky manual audits a thing of the past?

GE Healthcare and NVIDIA today announced they will deepen their 10-year partnership to bring the most sophisticated artificial intelligence (AI) to GE Healthcare’s 500,000 imaging devices globally and accelerate the speed at which healthcare data can be processed.

Discussions about machine learning’s impact on radiology might begin with image interpretation, but that’s only the tip of the iceberg. When it comes to realizing the technology’s full potential, it’s like Bachman Turner Overdrive sang many years ago: You ain’t seen nothing yet.