Breast cancer detection in mammography using deep learning approach
There are several key points that get left out in AI radiology conversations such as this one:<p>1) Mammograms are not interpreted in a vacuum. In fact mammograms are usually the first in a long line of tests before a breast cancer or other diagnosis is ultimately made. In fact, it&#x27;s probably more accurate to refer to mammography as a screening exam for which patients need a biopsy rather than a diagnostic test for cancer (there are rare exceptions, but overall this point holds).<p>2) Speaking frankly as a radiologist myself, tests like mammograms aren&#x27;t even that good in terms of overall diagnosis. Thats why ultrasound, tomosynthesis and MRI are often used as supporting evidence and&#x2F;or alternative exams.<p>3) There is controversy over the overall utility of mammograms, particularly in the screening context. Radiologists more than anyone would like the sensitivity and specificity of these studies to be higher.<p>It strikes me that the people that push these &quot;radiology is ripe for disruption&quot; or &quot;AI outperforms radiologists&quot; hyperbolic arguments are clearly people that have never seen the inside of a clinic. I&#x27;m sure they love this rhetoric though when pitching to VCs or sitting around the conference table coming up with &#x27;breakthrough ideas&#x27; to turn into power-points for the other administrators.