Machine learning has made great strides in the last few years, and Convolutional Neural Networks have demonstrated some very impressive results in image and speech processing. There are some great opportunities to apply them to healthcare, but there are some significant challenges too.
Regulatory approval requires a deeper understanding of the network than just black boxes, and there is new mathementics being developed to do just that.
Understanding deep convolutional networks Stéphane Mallat Published 7 March 2016.DOI: 10.1098/rsta.2015.0203
Deep learning architectures have recently reemerged with the advent of new computational resources and algorithmic improvements. They are yielding remarkable state of the art results in numerous learning tasks, primarily in computer vision for the analysis of images, speech recognition, and natural language processing [...] However, the complexity of such algorithms means that they have remained essentially a black box, yielding proportionally little insight given their performance achievements, which limits their utility in fields outside of those traditionally tackled by the machine learning community and obstructs new scientific directions. My research aims to open this blackbox by utilizing tools from harmonic analysis to construct multiscale deep learning architectures amenable to mathematical analysis.
Despite the challenges, devices based on neural networks have recently been granted FDA approval, and there are many startups emerging in this space.