Radiology is the medical specialty most conducive to clinical AI applications. After all, the pre-AI technique of computer-aided detection has been used in mammography since 1998, for example. So it shouldn’t come as a surprise to find AI “app stores” rising in radiology.

Researchers in Texas and Taiwan have collaborated to develop a deep-learning tool that can precisely asses the risk of breast cancer—and with it the need for biopsy—in patients with lesions of questionable concern found in mammograms.

Radiology, the medical specialty into which AI has made the furthest initial inroads in the U.S., is embracing the technology in France. And this is so despite French radiologists feeling underinformed on AI up to now.

Researchers have identified an approach for more accurately diagnosing schizophrenia using AI, bringing some objectivity to the field of neurodevelopmental disorder diagnosis, according to a study published in the May edition of Artificial Intelligence in Medicine.

A combination of the established denoising algorithm NeighShrink and chi-square unbiased risk estimation (CURE) could reduce noise in magnetic resonance (MR) images more effectively than traditional methods, according to research published in Artificial Intelligence in Medicine.

The American College of Radiology Data Science Institute (ACR DSI) introduced a new software platform April 5 aimed at better engaging radiologists in the creation, validation and use of AI models.

A team at Weill Cornell Medicine has developed an AI algorithm that can identify whether human embryos fertilized in vitro have the potential to progress to successful pregnancies, offering guidance as few as five days after an embryo is implanted.

Physicians and scientists in Germany have developed an artificial neural network that’s capable of interpreting brain MRI scans to tell physicians how brain tumors are responding to chemotherapy and radiation therapy, according to a study published in The Lancet Oncology.