Take radiology: there is some evidence that AI can sometimes identify solid-mass tumors that some radiologists miss. Look, I’ve got cancer. Thankfully, it’s very treatable, but I’ve got an interest in radiology being as reliable and accurate as possible.
Let’s say my hospital bought some AI radiology tools and told its radiologists: “Hey folks, here’s the deal. Today, you’re processing about 100 X-rays per day. From now on, we’re going to get an instantaneous second opinion from the AI, and if the AI thinks you’ve missed a tumor, we want you to go back and have another look, even if that means you’re only processing 98 X-rays per day. That’s fine, we just care about finding all those tumors.”
If that’s what they said, I’d be delighted. But no one is investing hundreds of billions in AI companies because they think AI will make radiology more expensive, not even if that also makes radiology more accurate. The market’s bet on AI is that an AI salesman will visit the CEO of Kaiser and make this pitch: “Look, you fire nine out of 10 of your radiologists, saving $20m a year. You give us $10m a year, and you net $10m a year, and the remaining radiologists’ job will be to oversee the diagnoses the AI makes at superhuman speed – and somehow remain vigilant as they do so, despite the fact that the AI is usually right, except when it’s catastrophically wrong.
“And if the AI misses a tumor, this will be the human radiologist’s fault, because they are the ‘human in the loop’. It’s their signature on the diagnosis.”