In multiple mammography datasets with the original radiologist-detected abnormality removed, deep learning detection of breast cancer had an average area under the curve (AUC) of 87 percent and an accuracy rate of 83 percent, according to research presented at the recent Society for Imaging Informatics in Medicine (SIIM) conference.

In a recent video interview from the Society for Imaging Informatics in Medicine (SIIM) conference, Ali Tejani, M.D., discussed pertinent insights on leveraging the value of adjunctive artificial intelligence (AI)-enabled triage software for computed tomography (CT) scans with radiology workflow improvements to achieve “clinically meaningful change” for patients with incidental pulmonary emboli findings.

In a video interview, Morris Panner, the president of Intelerad Medical Systems, discussed key observations from the recent Society for Imaging Informatics in Medicine (SIIM) conference, recent research about artificial intelligence (AI) adoption and emerging goals for enhancing the efficiency of radiology workflows.

In a recent video interview, abdominal radiologist Sonia Gupta, MD discussed key principles in assessing potential alliances with artificial intelligence (AI) vendors and the potential of AI to alleviate the time-consuming, administrative aspects of patient care.

The deep learning model may offer enhanced sensitivity and specificity on MRI for patients with glioblastoma, according to preliminary research presented at the Society for Imaging Informatics in Medicine (SIIM) conference.

The new workload management system reportedly emphasizes predictive analytics to facilitate efficient workload distribution and increase reading capacity for radiologists.

A 3D whole brain convolutional neural network could provide enhanced sensitivity and specificity for diagnosing intracranial hemorrhages on computed tomography, according to new research presented at the Society for Imaging Informatics in Medicine (SIIM) conference in Kissimmee, Fla.

What you need to address for successful artificial intelligence adoption and enterprise imaging integration.

Wearing a mask does cause an increase in report errors with speech-recognition software, but the mistakes are small and unlikely to cause a negative impact.

Augmenting generalizability of automated methods with local fine-tuning can help providers diagnosis osteoporosis with these scans.

Study results show DCNN can use imaging biomarkers to predict sex, potentially confounding the accurate prediction of disease.

Working with clinical and business leaders to create a standard process for point-of-care ultrasound opened the door for greater utilization and revenue capture.

Images can be skipped with monitors with refresh rates of both 60 Hz and 120 Hz.

Choosing a vendor for an enterprise imaging solution can be complex. Here’s what you need to inquire about to make the best choice for your organization.

15 tips for being an ideal mentor or mentee.

3D Convolutional neural network can effectively replace manual segmentation of the pancreas with or without cancer.

Using a deep convolutional neural network can improve providers’ ability to predict how patients with hepatocellular carcinoma will respond to Y90-RE treatment, enabling them to offer additional therapies, if necessary.

Incorporating artificial intelligence with TI-RADS improves sensitivity, specificity, and reduces interpretation time by nearly one-fourth.

Konica Minolta leaders outline the challenges to – and discuss tech solutions for -- provider and IT productivity during SIIM2020.

Internal reporting app, designed for radiologists, increased provider participation and captured incidents in a greater number of categories.