A machine learning-based “red dot” triage system could help differentiate between normal and abnormal chest radiographs while optimizing clinician workflow, British researchers reported this month in Clinical Radiology.
The American Society of Neuroradiology (ASNR) announced that Peter Chang, MD, a neuroradiology fellow at the University of California San Francisco, has received the Cornelius G. Dyke Memorial Award for his recent research involving deep learning technologies.
Israeli medical imaging startup Zebra Medical Vision has raised $30 million series C venture capital funds to create artificial intelligence (AI)-based tools for radiologists. At present, Zebra has raised a total of $50 million in funds.
Radiology professionals working on artificial intelligence (AI) technologies can learn a lot from studying self-driving vehicles, according to a new commentary published in the Journal of the American College of Radiology.
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.
A new partnership between University College London Hospitals and the Alan Turing Institute aims to start using artificial intelligence (AI) to perform certain tasks typically carried out by doctors and nurses.
Radiologists, clinical oncologists and industry stakeholders gathered May 16 in London to discuss artificial intelligence (AI) in medical imaging and cancer treatment. The all-day event was organized by the Royal College of Radiologists (RCR) with help from the Alan Turing Institute, Health Data Research UK and the Engineering and Physical Sciences Research Council.
Swapping traditional paper checklists for digital alternatives could cut the time physicists and dosimetrists spend on quality assurance (QA) within radiation therapy, researchers have reported in Practical Radiology Oncology. But it’s still unclear whether an electronic approach will really improve patient safety or quality of care.
Thought leaders within radiology largely agree that the specialty is in a unique position to help lead the implementation of artificial intelligence (AI) into clinical practice. But how, exactly, does that happen?
In a profession where success hinges on quality imaging surveillance, a lack of universally accepted guidelines often gives way to inconsistent reporting in radiology rooms—but a group of clinicians thinks artificial intelligence could be cleaning up workflow.
Charles E. Kahn Jr., MD, MS, professor and vice chair of the department of radiology at the University of Pennsylvania’s Perelman School of Medicine in Philadelphia, has been named the editor of RSNA’s new online journal, Radiology: Artificial Intelligence.
Anant Madabhushi, PhD, a professor at Case Western Reserve University in Cleveland, has led significant deep learning research in recent years, but he doesn’t necessarily think this evolving technology will replace radiologists and pathologists any time soon.