“Machine learning is a low-cost and efficient tool that could help physicians arrive to a quicker decision as to how to approach an indeterminate nodule,” lead author of the study, John Eisenbrey, PhD, said.

Researchers have detected racial bias in an algorithm commonly used by health systems to make decisions about patient care, according to a new study published in Science.

“This technology has incredible potential to improve care, whether it is by better training doctors to perform procedures or helping patients know what to expect when they arrive at the hospital,” Ziv Haskal, creator of the technology said.

Pairing multispectral optoacoustic tomography with ultrasound provided biomarker information that may better diagnose patient's with autoimmune diseases and thyroid nodules.

The commercially available algorithm helped residents improve their sensitivity at spotting abnormal findings in chest x-rays.

Mammography does a good job detecting calcifications, but its specificity for distinguishing benign from malignant findings remains low.

“Given the large number of people who suffer from traumatic brain injury every day and are rushed to the emergency department, this has very big clinical importance," study authors said.

A deep-learning algorithm can be as effective—or more effective—than radiologists in finding intercranial hemorrhages on CT scans.