Please: there ought to be a rule on HN that instead of linking to a popsci re-interpretation of a scientific result, you instead link directly to the paper.

http://stm.sciencemag.org/content/3/108/108ra113.full

If it is behind a paywall, as it probably is for this paper if you are not at a university, perhaps look around for the least hyperbolic re-interpretation of the paper, and link that instead.

http://www.genengnews.com/gen-news-highlights/image-analysis...

Even if you do not have access to the full paper because of the paywall, you should be able to still read the abstract and pick the popsci article that is fairest to what the paper actually says.

Otherwise, this happens: http://www.phdcomics.com/comics.php?f=1174 and I really don't like it when HN blindly follows the hivemind in furthering that phenomenon.

Let's examine what the authors actually say.

To directly compare the performance of the C-Path system to pathological grading on the exact same set of images, we applied standard pathological grading criteria to the TMA images used in the C-Path analysis. [...] the pathologist grading the images was blinded from the survival data. Although the C-Path predictions on the NKI data set were strongly associated with survival, the pathologic grade derived from the same TMA images showed no significant association with survival (log-rank P = 0.4), highlighting the difficulty of obtaining accurate prognostic predictions from these small tumor samples.

That's it. They make one remark about it, and do not focus on this at all elsewhere in the paper, because it was not the point of their study and the methodology for this little result is far from robust. Note they used one pathologist to run this little test. Also note that a high p-value is not evidence that the null hypothesis is true--it is quite possible that there is a relationship but the study is underpowered; this is a frequent point of confusion.

Let's please keep scientific statements in context. The original paper says nothing tantamount to computers diagnosing breast cancer more accurately than doctors. It is, principally, about a new morphological feature that the researchers believe is tied more strongly to survival according to their computational model.