A team of experts determined that correlating masses initially detected on MRI are significantly more likely to result in a cancer diagnosis than other common findings.
The model incorporates specific data from MRI exams with patient risk factors to predict whether a person is likely to develop clinically significant prostate cancer.
Finely tuned, pre-trained large language models are beginning to reliably translate image content into text, but are they ready to take on medical images?
Data from nearly 9.9 million participants were included in the team’s final analysis. The group emphasized the important role governments can play in trying to reverse the world's reliance on these products.