After using deep learning on patient scans to track cancer evolution, one research team is hopeful the “promising results” can help improve treatment response and survival predictions for cancer patients.
Collaborative community of developers, publishers, data scientists, and radiologists can seamlessly create, distribute, and utilize continuously learning algorithms for 25,000 radiologists across 5,500 connected healthcare facilities
A physician whose research produced promising results for using AI to improve the detection of tuberculosis (TB) was awarded the Alexander R. Margulis Award for Scientific Excellence during the annual RSNA conference in Chicago.
University of Oxford researchers were able to predict a patient’s risk of being admitted into emergency care by using machine-learning techniques with electronic health records (EHRs), according to a study published in PLOS Medicine.
A deep-learning algorithm was significantly faster and just as accurate as most radiologists in analyzing chest X-rays for several diseases, according to a study led by Stanford University researchers.
With the help of machine learning, researchers were able to train a computer to analyze breast cancer images and classify tumors accurately, according to a study published in NPJ Breast Cancer.
After receiving FDA clearance for AI software that can detect brain bleeds from CT images, MaxQ AI has announced a deal to integrate the software with medical imaging platforms.
