[D] DeepMind’s breast cancer screening does cite the earlier NYU work with ‘small, enriched datasets with limited follow-up’ critique
For those wanting to juxtapose the breast cancer screening work published by Deepmind with the earlier work published by the NYU team, the Deepmind paper does cite the NYU paper (twice) and here is the context in which the citations appear:
A few studies have characterized systems for breast cancer prediction with stand-alone performance that approaches that of human experts[29,30]. However, the existing work has several limitations. Most studies are based on small, enriched datasets with limited follow-up, and few have compared performance to readers in actual clinical practice—instead relying on laboratory-based simulations of the reading environment. So far there has been little evidence of the ability of AI systems to translate between different screening populations and settings without additional training data. Critically, the pervasive use of follow-up intervals that are no longer than 12 months [29,30,32,33]
 Wu, N. et al. Deep neural networks improve radiologists’ performance in breast cancer screening. IEEE Trans. Med. Imaging https://doi.org/10.1109/TMI.2019.2945514 (2019).
Can someone with any sort of radiology background here comment on the ‘ follow-up intervals’ part?
Pro-tip: The nature paper is not exactly behind a paywall (unless you try and access it via their main portal) and one can access the pdf via this page: