Healthcare, including imaging, is one of 13 industries that will soon be “revolutionized" by artificial intelligence (AI) and machine learning (ML) technologies, according to a new report published in Forbes.
Artificial intelligence (AI) can be trained to predict a patient’s likelihood of axillary lymph node metastasis using a breast MRI dataset, according to a study published in the Journal of Digital Imaging.
Researchers at Duke University have been awarded a $196,000 grant to address a growing issue related to the use of artificial intelligence (AI) in healthcare: the gray area between explaining decisions to patients and protecting trade secrets associated with clinical decision support (CDS) software.
Various companies are working with the National Health Service (NHS) in England to see if their artificial intelligence (AI) technology can identify signs of breast cancer as well as radiologists, according to a report from the Financial Times.
How significant is the hype surrounding artificial intelligence and machine learning in radiology? According to new market research from Reaction Data, 77 percent of imaging professionals said they think machine learning is important when asked about it in 2018, up from 65 percent in 2017.
Researchers have successfully used two different machine learning algorithms to predict three common symptoms—sleep disturbance, anxiety and depression—experienced by cancer patients undergoing chemotherapy. The team's findings were published in PLOS One.
Artificial intelligence (AI) technologies have the potential to help radiologists demonstrate their value by establishing stronger emotional connections with patients, according to a new analysis published in the American Journal of Roentgenology.
Aidoc and the American College of Radiology Data Science Institute (ACR-DSI) are now helping artificial intelligence (AI) researchers track the performance of various algorithms with an assist from Nuance's PowerScribe Workflow Orchestration platform.
Artificial intelligence (AI), especially machine learning (ML), is destined to play a key role in the future of interventional radiology (IR), according to the authors of a new study published in the Journal of Vascular and Interventional Radiology.