<story><title>DeepMind readies first commercial product</title><url>https://www.ft.com/content/0e099914-514a-11e9-9c76-bf4a0ce37d49</url></story><parent_chain><item><author>melling</author><text>AI is being developed to make MRI scans 10x faster.&lt;p&gt;&lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;www.forbes.com&amp;#x2F;sites&amp;#x2F;samshead&amp;#x2F;2018&amp;#x2F;08&amp;#x2F;20&amp;#x2F;facebook-aims-to-make-mri-scans-10x-faster-with-nyu&amp;#x2F;#5a5f7fd87a04&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;www.forbes.com&amp;#x2F;sites&amp;#x2F;samshead&amp;#x2F;2018&amp;#x2F;08&amp;#x2F;20&amp;#x2F;facebook-ai...&lt;/a&gt;&lt;p&gt;If DeepMind is trained on millions of MRI’s, we might have better preventative medicine.</text></item></parent_chain><comment><author>dontreact</author><text>I have talked at length with several people in the FastMRI project and in my opinion it is actually very dangerous. There is no feasible way to validate that such a model will not hallucinate normal tissue in the presence of a rare abnormality. The argument often used is that advanced reconstruction techniques such as compressed sensing have not required validating against very rare abnormalities, however when deep neural networks get involved you have a nearly universal function approximator whose behavior is nowhere near as bounded.&lt;p&gt;The FDA will probably set the low bar of validating that the reconstruction algorithm fares well in the face of a few abnormalities and call it a day, instead of the proper (admittedly infeasible) validation of testing against all abnormalities that the reconstruction algorithm will encounter. Facebook will probably happily jump over this low bar, and patients will get hurt.</text></comment>
<story><title>DeepMind readies first commercial product</title><url>https://www.ft.com/content/0e099914-514a-11e9-9c76-bf4a0ce37d49</url></story><parent_chain><item><author>melling</author><text>AI is being developed to make MRI scans 10x faster.&lt;p&gt;&lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;www.forbes.com&amp;#x2F;sites&amp;#x2F;samshead&amp;#x2F;2018&amp;#x2F;08&amp;#x2F;20&amp;#x2F;facebook-aims-to-make-mri-scans-10x-faster-with-nyu&amp;#x2F;#5a5f7fd87a04&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;www.forbes.com&amp;#x2F;sites&amp;#x2F;samshead&amp;#x2F;2018&amp;#x2F;08&amp;#x2F;20&amp;#x2F;facebook-ai...&lt;/a&gt;&lt;p&gt;If DeepMind is trained on millions of MRI’s, we might have better preventative medicine.</text></item></parent_chain><comment><author>krapht</author><text>&amp;quot;Using AI, it may be possible to capture less data and therefore scan faster, while preserving or even enhancing the rich information content of magnetic resonance images, says Facebook.&lt;p&gt;The key will be to train artificial neural networks to recognise the underlying structure of the images in order to fill in detail omitted from an accelerated scan.&amp;quot;&lt;p&gt;Ah, right, what I want is for an ANN to invent information in a medical image. It&amp;#x27;s one thing to upscale textures in a game, but I can&amp;#x27;t see the use for this at all in a medical imaging device.</text></comment>