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?

Mayo Clinic is expanding its library of clinical insights by enabling other healthcare organizations to access and deliver digital content across the clinical and operational aspects of a patient care journey.

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.

Researchers have used AI technology to predict a patient’s chance of death, heart attack or stroke better than human doctors, sharing their findings in a new study in Circulation.

AI models can be trained to predict outcomes in meningioma patients, according to new research published in npj Digital Medicine. The study’s authors even developed a free smartphone app so others can explore their work.

AI promises to make a titanic impact on radiology, but most of the attention tends to focus on its ability to identify important findings in medical images. What about the technology’s non-interpretive qualities?