Health Imaging spoke with Eliot Siegel, MD, before this year’s SIIM Conference on Machine Intelligence in Medical Imaging to learn more about challenges and opportunities related to artificial intelligence (AI) in medical imaging, how AI and machine learning should be incorporated into clinical applications and what the future holds for AI.

Artificial intelligence (AI)-focused startup Qure.ai published a study Tuesday, Sept. 18, validating its chest x-ray algorithm trained on 1.2 million chest scans and radiology reports.

An artificial intelligence (AI) algorithm created by NYU School of Medicine researchers distinguished between two forms of lung cancer with 97 percent accuracy, according to a study published in Nature Medicine.

Researchers have created a deep-learning model that can generate synthetic brain MRIs to help train neural networks, according to research published in ArXiv. The technique may provide reliable, shareable training data.

An artificial intelligence (AI) system developed by Shinjini Kundu, PhD, a physician and medical researcher at the University of Pittsburgh Medical Center, could find patterns of developing diseases as much as three years earlier than imaging experts.

Researchers from Tel Aviv University in Israel created a new microscopy method that utilizes an add-on for laser scanning microscopes to improve the quality of 2D and 3D brain imaging, according to research published Sept. 13 in Optica.

Engineers from the University of British Columbia (UBC) in Canada who developed a new ultrasound transducer say it could lower the cost of ultrasound machines to just $100. The probe is portable, wearable and can be powered by a smartphone.

Research published online Sept. 3 in Scientific Reports concluded that an artificial intelligence (AI) algorithm can differentiate between tuberculous (TB) spondylitis and pyogenic spondylitis on MRI exams with the same level of expertise as skilled musculoskeletal radiologists.