Healthcare AI giant IBM Watson Health has introduced software that can help medical researchers design clinical studies right the first time so as to minimize the need for tweaking and fine-tuning while their study is underway.

Researchers have developed two AI-powered tools for automatically extracting key information from free-text pathology reports. The team shared its findings in the Journal of the American Medical Informatics Association.

Machine learning-based CT texture analysis can help with the evaluation of solid renal masses, according to new findings published in Academic Radiology. Could this help reduce the number of patients undergoing unnecessary surgeries?

AI technology could replace countless jobs in the not-so-distant future, making an impact on workforces all over the world. According to a new analysis published in Information and Organization, researchers and policymakers alike should pay especially close attention to this development and get involved now—before it’s too late.

Numerous deep learning models can detect and classify imaging findings with a performance that rivals human radiologists. However, according to a new study published in the Journal of the American College of Radiology, many of these AI models aren’t nearly as impressive when applied to external data sets.

When Brevin Cronk found himself in an emergency room last December, his blood-oxygen level was 77% and his lips had turned blue. It was soon determined by the team at UW Medical Center in Seattle that a transcatheter repair was necessary—and virtual reality (VR) played a key role in Cronk’s care.