Production AI systems are hard
I'm just finishing interventional radiology training and I moonlight as a diagnostic radiologist (not to mention having an undergrad/master's in computer science).<p>Almost 90% of the diagnostic studies I read could be pre-drafted by AI. That's where the money is and where AI-in-radiology companies should focus. The money is <i>not</i> in detecting hemorrhage or pulmonary embolism. It's a classic fallacy to think that life-saving means money-saving. Rather, the money in radiology is reads per day.<p>Here's a user story: A private practice radiologist reads 20 abdomen and pelvis CT scans with contrast per day. In each of these studies, he must write a short description of each organ. For example, "Gall bladder: Unremarkable" or "Gallbladder: Cholecystitis without evidence of cholecystitis" or "Gallbladder: Dependent sludge." There are around 15 such organs (liver, gallbladder, pancreas, spleen, adrenal glands, kidneys, etc.). The AI should auto-populate the radiologist report with an appropriate description of each organ system.<p>The job of the radiologist is to confirm what the AI says in each section, and to go into further detail as needed. It's essentially just customizing the existing template to each patient. This type of pre-drafting is exactly what radiology residents do and what companies like vRad do.