Researchers revealed that less than 1% of more than 46,000 patients took advantage of online self-scheduling tools.

Homogenous datasets can create unintended research bias that hinders the clinical efficacy of AI applications, experts recently explained in PLOS Digital Health.

A study in Lancet Digital Health reports that a previously validated, high performing AI model committed troublesome errors when confronted with atypical anatomy.

The new algorithm flags chest x-rays when it detects pneumothorax and notifies physicians of any suspicious findings.

Research published recently in Radiology found comparable sensitivity and specificity between artificial intelligence and clinicians for fracture detection. 

 "This research aims to raise a red flag regarding naive off-label usage of open-access data in the development of machine-learning algorithms," experts involved in the study said.

The availability of clinical histories is beneficial to radiologists but has previously been shown to create bias and increase false positives in some cases.

First there was PACS: picture archiving and communications systems. Over the last decade, as managing medical imaging has expanded far beyond radiology, enterprise imaging was born. But what is enterprise imaging in its best form?