The majority of radiology trainees have expressed interest in furthering their knowledge of artificial intelligence applications in the field, yet few are offered the opportunity to do so during the course of their education, according to new survey data.

A multi-modal AI approach can combine information from both ABUS and DM, which could be especially beneficial in resource poor regions where experienced radiologists might not be readily available.

Here is a list of some key trends in radiology technology from our editors based on our coverage of the radiology market.

The Edinburgh-based business made the announcement on Jan. 18, noting that the acquisition will build on the company’s goals to “improve the lives of patients and populations by unlocking the adoption and benefits of medical imaging AI.” 

The deep learning model was trained to predict risk of lung cancer in the one to six years following completion of an LDCT scan, and it does not require clinical information relative to risk factors to do so.

Many radiologic technologists strongly feel that AI technologies should be incorporated into the curriculum for today’s emerging radiographers. 

The models do not require manual measurements from a sonographer to estimate GA. Instead, they are able to make use of ultrasound images and videos.

Out of 10 mock exams, the AI candidate passed two, achieving an overall accuracy of 79.5%, suggesting that the candidate is not quite “ready to graduate.”