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.”
The results of a survey completed by more than 13,000 respondents who were eligible for the cancer screening revealed that less than 2% of eligible participants underwent CTC exams.
Researchers reported that the artificial intelligence system was able to interpret more than 114,000 screening mammograms using a reading protocol with high sensitivity and specificity.
These findings underscore the need for better implementation of shared decision-making (SDM) models, as well as more thorough counseling documentation, as low-dose CT (LDCT) lung screen coverage is dependent on these factors, experts suggested.
The downward trend in annual mammography adherence should serve as a call to action for new processes to engage breast cancer survivors, physicians urged.