Breast artery calcifications are already visible when radiologists review mammograms, but nothing typically happens with them. Researchers aimed to see if AI could help translate those findings into an easy-to-understand cardiovascular risk score.

Despite the great progress that has been made toward the clinical implementation of AI, new data caution against trusting the technology as a single reader in certain settings.

The ACR hopes these changes, including the addition of diagnostic performance feedback, will help reduce the number of patients with incidental nodules lost to follow-up each year.

Not all AI or regression models are the same, nor do they all incorporate the same data when assessing patient risk.

And it can do so with almost 100% accuracy as a first reader, according to a new large-scale analysis.

Ultrasound is routinely used to screen for HCC. However, its utility is limited by numerous factors, including patient body habitus, operator experience and certain liver conditions, all of which contribute to decreases in sensitivity. 

During this year's RSNA meeting, providers were especially interested in how AI applications could enhance opportunistic screening capabilities.

In conjunction with prevention efforts, the introduction of screening examinations has resulted in a reduction of nearly 6 million cancer-related deaths since 1975.