[dkonofalski]

While I understand and mostly agree with your general sentiment, I think you're downplaying the extent of how valuable the machine learning is in this process. The reason why we need all these sensors and machines currently is because we need a reasonable picture of how all that data works together so that a human being can look at that picture as a whole and make some deductions.

This isn't the first time or the last time that computers have been able to find patterns in much simpler measurements simply because they're not human. A similar example, although not quite as advanced, is the ability for a computer to extract sound information from a black and white video. Computers have been able to recreate sounds from behind double-paned glass by analyzing vibrations captured through videos. Humans have had to rely on various arrays of lasers, sonar, and other directional instruments to get 1/10th of the accuracy that a computer algorithm has been able to achieve from a simple, low-quality video camera. The point is that more machines and sensors doesn't always equal a better diagnosis. Better analysis of existing sensors and tech, even if it seems to be lower fidelity, can actually yield more accurate results.