Machines can now be trained to see things humans cannot, and likely never will. Researchers have recently demonstrated this principle in applications involving a wide range of biomedical imaging. From obviating the need to stain pathology slides, to finding rare cells without cytometry, to characterizing skin lesions, retinal scans, chest X-rays, brain CT scans, heart MRIs, and much more, AI stands to change the way we do medicine (See “Artificial Intelligence Sees More in Microscopy than Humans Do,” The Scientist, May 2019).
This advance relies on deep neural networks, systems of artificial neurons that can accurately and rapidly detect complex patterns. It’s an approach to artificial intelligence (AI) that has gathered remarkable momentum since it was introduced about a decade ago, and today chiefly relies on supervised learning—that is, the ground truths of accurately labelled images that are used to train the network. And so-called deep learning is proving its ...
