A new imaging technique that uses deep learning technology can identify tumors in colorectal tissue samples with 100% accuracy, according to findings published in Theranostics.

AI models can be trained to evaluate chest x-rays as well as radiologists, according to a new study published by Radiology. Specialist-approved reference standards played a crucial role in the team’s research.

Patients getting chest CT scans for lung cancer screenings can also be measured for heart disease thanks to AI, according to a new study presented at RSNA 2019 in Chicago. 

Deep learning-based prediction models can help healthcare providers diagnose small pulmonary nodules, according to a new study published in Academic Radiology.

Convolutional neural networks (CNNs) can be trained to detect lung nodules on chest x-rays, according to a new study published in Artificial Intelligence in Medicine.

The United States is expected to experience a massive shortage of psychiatrists within the next five years. According to some researchers, however, AI technology could ease some of that burden.

Lunit, a medical software company based out of South Korea, has gained CE certification for its newest chest x-ray analysis solution, Lunit INSIGHT CXR.

Deep learning models can be trained to predict lymph node metastasis in breast cancer patients, according to new findings published in Radiology.

AI can provide significant value to radiologists by sending urgent imaging studies to the top of their worklists, according to a new analysis published in Academic Radiology.

The rapid rise of AI could potentially change healthcare forever, leading to faster diagnoses and allowing providers to spend more time communicating directly with patients. According to a new report from the Brookings Institution, however, there are also risks associated with AI in healthcare that must be addressed.

Fifty-three percent of physicians say they are optimistic about AI’s potential effect on healthcare, according to a new survey of more than 1,700 physicians published by the Doctors Company.

Machine learning can help electrophysiologists or other heart specialists decide whether a patient is a good candidate for a pacemaker or implantable cardioverter defibrillator, according to a study published Oct. 3 in PLOS One.