<instructions>Your goal is to analyze the following comment and estimate how highly it will be upvoted by the Hacker News community.</instructions><comment><author>neuro_image3</author><text>There are several key points that get left out in AI radiology conversations such as this one:&lt;p&gt;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&amp;#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).&lt;p&gt;2) Speaking frankly as a radiologist myself, tests like mammograms aren&amp;#x27;t even that good in terms of overall diagnosis. Thats why ultrasound, tomosynthesis and MRI are often used as supporting evidence and&amp;#x2F;or alternative exams.&lt;p&gt;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.&lt;p&gt;It strikes me that the people that push these &amp;quot;radiology is ripe for disruption&amp;quot; or &amp;quot;AI outperforms radiologists&amp;quot; hyperbolic arguments are clearly people that have never seen the inside of a clinic. I&amp;#x27;m sure they love this rhetoric though when pitching to VCs or sitting around the conference table coming up with &amp;#x27;breakthrough ideas&amp;#x27; to turn into power-points for the other administrators.</text><parent_chain></parent_chain></comment><story><title>Breast cancer detection in mammography using deep learning approach</title><url>https://arxiv.org/abs/1912.11027</url></story>
<instructions>Your goal is to analyze the following comment and estimate how highly it will be upvoted by the Hacker News community.</instructions><comment><author>RcouF1uZ4gsC</author><text>Of the major specialties, it seems that radiology is the most in danger of significant disruption. First of all, it can be done remotely, so there is risk if the regulation is lightened that foreign radiologists will be allowed to read studies at much less cost. The other issue is that this is something that deep learning can rapidly progress in given there are already a plethora of labeled data sets. For example, every mammogram that is taken has already been labeled normal or abnormal.</text><parent_chain></parent_chain></comment><story><title>Breast cancer detection in mammography using deep learning approach</title><url>https://arxiv.org/abs/1912.11027</url></story>