Diagnostic screening programs help catch cancer, abnormalities or other diseases before they reach an advanced stage, saving lives and healthcare costs. Screening programs include, lung, breast, prostate, and cervical cancer, among many others.
New findings support the routine use of deep learning-based risk assessments, as this method can decrease subjectivity, reduce unnecessary imaging and improve diagnostic accuracy.
The COlorectal Cancer detection with AI, or COCA, model is a cost-effective, scalable solution that turns routine CT scans into opportunistic exams that can be used to proactively identify CRC.
Two respected radiology organizations have issued a stark warning on the new recommendations, stating that they risk confusing patients and “may contribute to thousands of additional breast cancer deaths each year.”
There are no standards requiring radiologists to report on the presence of BACs, even though up to half of referring providers have indicated they would prefer to be made aware of the finding.
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.
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.