I think this has more to do with the fact that Watson sucks than anything else, although it may be a harbinger for other efforts to sprinkle magic machine-learning pixie dust on complex problems in the hope for a solution.
I've talked to literally dozens of people / companies / academics running at problems in healthcare using deep learning and large data sets, and the recurrent theme is cowboys who pretty consistently fail to follow basic practices of experimental rigor. ML is frankly an area where more rigorous and clear regulation is desperately needed, because the potential to cut corners / cheat is so easy and the challenges of vetting these systems are specialized enough that most organizations won't successfully be able to catch fairly basic experimental mistakes.