This post is in response to the call for feedback regarding the “Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD)” document put forward by the FDA on Apr 2, 2019. Contents of this post were uploaded to the regulations.gov comments section on May 3rd, 2019.

We applaud the FDA’s initiative to address the life-long learning aspects of some AI systems. Follows is an excerpt from a recent review our lab published in Nature Review Cancer in May 2018. The excerpt outlines regulatory aspects related to governing AI-based system submissions seeking approval. …


This entire content is taken from here and I dont see any citation.


tl;dr Selecting the right initializer for your network/data/hyper-parameter combo can have a significant effect on your results, so test in smaller contained environments first and choose wisely!

Why? Adjusting optimizer-specific parameters such as the learning rate, batch size and number of epochs are up there on the list of things to tweak while training deep neural networks. Other architecture-specific hyper parameters are sometimes completely overlooked. This is often the case with high level api’s such as keras where many arguments are set to defaults and are rarely modified. Initialization is one of these overlooked hyper parameters. …


We’ve all been witnessing recently the massive amounts of social media posts and press reports around AI and its perceived future impact—from a total “doomsday” scenario to people losing their jobs. I wanted to address a few concerns that I felt were left out of the conversation or not reported on sufficiently. While it is naturally effective to think about the big picture and the general effects AI is bound to have on society at large, we should also consider such issues within its inner workings.

Air of mystery Coupled with the lack of concrete theory, the inherently opaque nature…


Wired Issue 16.07: The End of Science

It was in 2008 that Wired magazine’s editor-in-chief back then Chris Anderson predicted the end of theory and science in an article titled “The End of Theory: The Data Deluge Makes the Scientific Method Obsolete”. Anderson argued that “our ability to capture, warehouse, and understand massive amounts of data is changing science, medicine, business, and technology” . And in fact it did. The scientific approach includes creating a hypothesis, testing it and thus validating or disproving it. With massive amounts of data, this approach is obsolete. The access to data replaces the need to prove a hypothesis. Instead of carrying…

Ahmed Hosny

machine learning + medical imaging — www.ahmedhosny.com

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