Moore's Law, as a reminder, states that the number of transistors that can be placed inexpensively on an integrated circuit doubles approximately every two years. Today's iPhones and iPads are a product of 50 years of Moore's Law.
Topol's book starts out by celebrating the many advances that Moore's Law-driven technologies have brought to so many industries—with the notable exception of healthcare, an industry he characterizes as having "hardening of the arteries."
But in the pages that follow, I find many examples of suggested innovations that I just have a hard time believing are really here yet.
For every genome sequence mentioned in the book, one could ask: How many genomes remain unsequenced? And although the cost of sequencing is dropping, what does the total tab look like for sequencing all the genomes that need to be sequenced?
Since disease management is moving to a personalized perspective, that number is bound to be astronomical. And I'm aware that in treating things like tumors, the genome of the tumor can mutate in its evolution, requiring repetitive sequencing of just a single tumor. That sounds like a lot more sequencing to me. Even if, as Topol suggests at one point, we start by looking at every base of particular regions of the genome, it's a daunting task.
It's always a struggle to know how much of a particular present-day technology to teach to students. Topol suggests that medical students no longer need to learn much biochemistry and physics, and would instead substitute learning on genomics and social networking technology, among other things. It makes good copy, but is it good science?
I'm mildly surprised that a book this recent doesn't pay more attention to the impending role that machine-driven algorithms will play in decoding the secrets of genomic medicine. I question the utility of having one's genome to browse on an iPad, even though as Topol points out, a browser for this exists.