It COULD be used to craft a pipeline that dramatically improved everyone's health. It would take probably a decade or two of testing (an annual MRI, an annual sequencing effort, an annual very wide blood panel) in a longitudinal study with >10^6 people to start to show significant reductions in overall cancer mortality and improvements in diagnostics of serious illnesses. The diagnostic merit is almost certainly hiding in the data at high N.
The odds are that most of the useful things we would find from this are serendipitous - we wouldn't even know what we were looking at right now, first we need tons of training data thrown into a machine learning algorithm. We need to watch somebody who's going to be diagnosed with cancer 14 years from now, and see what their markers and imaging are like right now, and form a predictive model that differentiates between them and other people who don't end up with cancer 14 years from now. We [now] have the technology for picking through complex multidimensional data looking for signals exactly like this.
In the meantime, though, you have to deal with the fact that the system is set up exclusively for profitable care of well-progressed illnesses. It would be very expensive to run such a trial, over a long period of time, and the administrators would feel ethically bound to unblind and then report on every tiny incidentaloma, which completely fucks the training process.
This US is institutionally unable to run this study. The UK or China might, though.
The child of a friend of mine has PTEN-Hamartom-Tumor-Syndrom, a tendency to develop tumors throughout life due to a mutation in the PTEN gene. The poor child gets whole body MRIs and other check-ups every half year. As someone in biological data science, I always tell the parents how difficult it will be to prevent false positives, because we don't have a lot of data on routine full body check-ups on healty people. We just know the huge spectrum on how healthy/ok tissue looks like.
I know that Python is used for many more things than just data science, so I'd love to hear if in these other contexts, a pipe would also make sense. Just trying to understand why the pipe hasn't made it into Python already.