just like how lumberjacks get worse at using an axe after leaning on chainsaws.
Edit:
just to be a little less facetious, i’ll note that this is not related to the current ai hype at all. the medical field has been using machine learning for well over two decades at this point, and generally in the form of classifiers rather than generators. you feed it a bunch of x-rays and whether they show, say, lung cancer, and the system will automatically sort out things that look normal from things that don’t. this is a good thing because it means doctors can spend more time with patients. doctors also got worse at manually diagnosing broken bones when x-ray machines became common.
Edit 2:
a classifier basically just cooks an image down to some basic characteristics, then places it on a graph, and checks if it’s above or below a line it has used other images to refine. it looks like this:
say blue dots, are images that don’t show lung cancer, and red dots are images that do. where they end up in the graph is based some amount of factors that are determined by a medical professional. it doesn’t have to be 2D, it can be any number of dimensions. then, using one or more of the methods in the graph, the machine learning algorithm figures out where to draw the line between blue and red. then, when you feed in a new image, it can tell you whether it’s definitely in the blue area, and therefore normal, or maybe in the red area, and therefore worth a closer look by a doctor.
just like how lumberjacks get worse at using an axe after leaning on chainsaws.
Edit:
just to be a little less facetious, i’ll note that this is not related to the current ai hype at all. the medical field has been using machine learning for well over two decades at this point, and generally in the form of classifiers rather than generators. you feed it a bunch of x-rays and whether they show, say, lung cancer, and the system will automatically sort out things that look normal from things that don’t. this is a good thing because it means doctors can spend more time with patients. doctors also got worse at manually diagnosing broken bones when x-ray machines became common.
Edit 2:
a classifier basically just cooks an image down to some basic characteristics, then places it on a graph, and checks if it’s above or below a line it has used other images to refine. it looks like this:
say blue dots, are images that don’t show lung cancer, and red dots are images that do. where they end up in the graph is based some amount of factors that are determined by a medical professional. it doesn’t have to be 2D, it can be any number of dimensions. then, using one or more of the methods in the graph, the machine learning algorithm figures out where to draw the line between blue and red. then, when you feed in a new image, it can tell you whether it’s definitely in the blue area, and therefore normal, or maybe in the red area, and therefore worth a closer look by a doctor.