Artificial Intelligence

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.

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.

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.

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?

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.