Another point is that it's more dangerous to use deep-learning methods like this in highly regulated and sensitive environments like the healthcare industry for medical diagnosis. Medical devices involved in patient diagnosis are required to be transparent in its decision process in order to understand how it came to its decision. Despite CNNs having highly accurate results, its pretty much as useful as a black box to a doctor or medical professional if it cannot explain itself, which is one part of the hype of the deep-learning approach and is an ongoing problem in the AI research.

There are biases that can occur in CNNs which can skew some results and worst of all, debugging these CNNs or deep-learning systems around "what went wrong" will be harder given its black box nature and will be done by tweaking the hyper-parameters to get a "good enough" result which is unacceptable to professionals in mission critical industries or those dealing with human life.