"Medical-grade AI" has been possible for some diagnosing tasks since 1968. A simple linear model that takes in some features from an X-ray was able to outperform the best doctor in one study: https://goo.gl/yMP7sU
There are three reasons Google open-sourced TensorFlow:
1. Their stated reason.
2. Branding. Google likes to show off advanced products that aren't fully developed in order to elevate their brand and recruit engineers (even though most do not work on such products). Project Soli is the best example of this: the product video shows off a small radar and some range-doppler video, but it does not show complicated gesture recognition capabilities, which is the hardest part.
3. Free labor. Deep learning algorithms require a ton of data in order to not overfit to the training data. Google has tons of data while academic researchers do not. If Google gets researchers to develop new algorithms that work well on smaller amounts of data and the researchers develop their algorithms in TensorFlow, then Google can easily incorporate the work into their own products and make it better.
I learned a lot about TCP (including delayed acks), the basics of 802.11, and the basics of switching fabrics in my undergrad networking class, so I wouldn't say it's totally unrealistic for someone to talk a little about those topics, depending on the role.