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.”
Generative artificial intelligence models have shown great potential for improving multiple aspects of the radiology field, but a new analysis cautions that they still require significant oversight.
Dana Smetherman, MD, CEO of the American College of Radiology, discusses the policy, which urges for more robust promotion of low-dose CT as a public health tool.
New research adds to the “strong evidence” supporting screening guidelines and highlights the importance of women adhering to clinical recommendations.
Advances in treatment are often credited with improving breast cancer outcomes, but new findings suggest the decrease in mortality may actually be due to improved screening initiatives.
John Simon, MD, CEO of SimonMed Imaging, says imaging has considerably advanced for noninvasive detection of disease and it may be time for it to play a greater role in annual physicals, especially in executive physical exams.
