Not only can different lung diseases look much the same in chest imaging, but distinct diagnoses may present widely dissimilar image patterns in the same patient at the same timepoint, too.

Actions taken at Changi General Hospital in Singapore led to a 17.2% improvement from the institution’s baseline missed appointment rate, authors highlighted Wednesday.

In a small group of women with the highest algorithm prediction risk scores, the tool could have spotted 27% of ensuing cancers, experts reported recently.

Experts at Georgia State University are using thousands of datasets and various imaging modalities to investigate bipolar disorder, schizophrenia and depression.

Researchers with Ben-Gurion University of the Negev in Israel presented their work at the 2020 International Conference on Artificial Intelligence in Medicine.

A machine learning algorithm proved highly accurate at diagnosing neurological disorders on MRI scans, experts out of Tokyo reported recently.

In total, 7,774 images taken from 287 patients were used to train the deep learning model, according to a study published in AJR.

The neural network requires only a fraction of the data typically needed for normal MR imaging exams.

It can also help trainees improve their scores on the Fundamentals of Laparoscopic test, which must be passed before receiving general surgery certification.

The hybrid platform proved more accurate at detecting enlarged heart cases than machine learning or a human reader working independently.

The algorithms will analyze various pieces of information, including CT images and vital signs, to help clinicians determine disease severity and predict patient outcomes.

Given that the disease is one of the most common cancers among women, these results may help personalize care plans and avoid invasive options for many struggling with treatment decisions, experts wrote in JAMA Network Open.