Skin lesions are still not peanuts. There are 0 FDA approved machine-learning diagnostics or other management technologies in dermatology. There is no widesprea

Skin lesions are still not peanuts. There are 0 FDA approved machine-learning diagnostics or other management technologies in dermatology. There is no widespread clinical use of CNNs for skin lesion diagnosis even at top-tier university hospitals. With more reliable studies, hopefully this will change. But in terms of eating the nuts after having proverbially cracked the shell with a couple of somewhat promising papers, we are 0% there yet in the clinic and there seems to be good reason for that. The cited 2017 Stanford study was a landmark in principle but (1) was not making an exceedingly relevant comparison and also (2) would need out of sample validation from other image sources for people to use it. Regarding (1), ideally one would compare CNN evaluation to a real-world clinical evaluation (evaluation of texture and extremely 'hi-res' magnified exam +/- polarized light), rather than to dermatologists also looking at a grainy 299x299 pixel image. But the preferred study would be difficult or impossible to coordinate, so at least regarding (2) having high out-of-sample performance would need to be proven, such as on sample from an entirely different institution or group of institutions, and that step has been a great stumbling block in many trained-networks in medicine. I don't know what the status for their network is. The other curious issue is representation. Even if the model performs well on 100,000 images from mostly-Caucasian patients, would I trust it if my skin were brown or black or had background dyspigmentation from a chronic skin disorder? Eek, not sure. A CNN training on patients from Japan should not be expected to work for skin lesions from randomly chosen people in South Africa or Sweden. This is a most obvious case for needing actively inclusive representation in neural network training. One way or another massive number of heterogeneously acquired images are needed from a huge range of people. For underrepresented skin types or for lesions in underrepresented contexts (overlapping with a scar or in a tattoo or on scrotal or perianal skin) many clinicians and astute patients would be happier if there was a Bayesian network with a prediction confidence interval rather than having to deal with whatever pops out of the softmax. The fields where there is more success at least in terms of getting technologies approved for use (but also probably in terms of more active research) are here, and many relate to radiology: https://www.nature.com/articles/s41746-020-00324-0/tables/2 https://www.nature.com/articles/s41746-020-00324-0/tables/2