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.”
Individuals exposed to diagnostic radiation in the first decade of their life face an increased risk of developing the disease, Penn Medicine experts reported recently.
Experts from a pandemic hotspot in Austria reported on the first patients enrolled in an ongoing study, noting significant imaging-based improvements after 12-week follow-up visits.
Patients with developmental difficulties are more likely to undergo a CT scan—rather than ultrasound—compared to those without cognition issues, researchers reported.
On-call trainees are a great resource during off-hours, but must avoid missing key organs during ultrasound exams to prevent unnecessary follow-up CT and MRIs, experts wrote in Academic Radiology.