Artificial intelligence (AI) in medical devices may lead to breakthroughs for self-management in patients with diabetes, according to a study published May 31 in the Journal of Medical Internet Research.

NSW Health in Australia has signed a Proof of Concept agreement with the international medical imaging IT and cybersecurity company Sectra, which has been selected as the preferred vendor for a large enterprise imaging IT solution in New South Wales following a competitive tender process.

Intelerad extends its partnership with Blackford to provide access to best-of-breed clinical applications and AI solutions worldwide via Blackford Platform.

FUJIFILM Medical Systems U.S.A., Inc. will participate in the Society for Imaging Informatics in Medicine's (SIIM) annual meeting in National Harbor, MD, May 31 – June 2, 2018.

When compared to the performance of 58 dermatologists from 17 different countries around the world, AI missed fewer melanomas and misdiagnosed benign moles less often, according to a study published in the Annals of Oncology.

Convolutional neural networks (CNN) were more accurate than even the most expert dermatologists in detecting skin cancer, researchers reported in Annals of Oncology this week.

Machine learning techniques perform well when tasked with predicting malignancy in breast lesions identified during breast cone-beam CT (CBCT) exams, according to a new study from German researchers published by the American Journal of Roentgenology. One technique, back propagation neural networks (BPN), outperformed two radiologists.

Researchers from the University of Wisconsin-Milwaukee have used a new imaging technique to visualize cellular damage to the retina caused by diabetes. The method, tested in a mouse model, may aid in understanding diabetic retinopathy—a leading cause of blindness.