The massive datasets that are now emerging in biomedicine have created an imperative to adopt machine learning and AI. Take, for example, the Cancer Genome Atlas of multidimensional biologic data, comprising various “omics” (genomics, proteomics, and so on). All told the atlas holds more than 2.5 petabytes of data generated from more than 30,000 patients. No human could wade through that much data. As Robert Darnell, an oncologist and neuroscientist at Rockefeller University put it, “We can only do so much as biologists to show what underlies diseases like autism. The power of machines to ask a trillion questions where a scientist can ask just ten is a game-changer.”
That said, unlike the immediate and ongoing changes that AI is unleashing on clinicians in the pattern-heavy medical fields like pathology and radiology, AI isn’t yet challenging the status quo for scientists in any significant way; AI is just here to ...
