Brain MRI's (in all their forms) are a whole different species than all other MRI's, due to variability and softness of structures, differences in condition pre

Brain MRI's (in all their forms) are a whole different species than all other MRI's, due to variability and softness of structures, differences in condition presentation/time-frame, and and the high incidence of brain MRI's in the elderly (the brain can shrink precipitously after 60). Also, neurologists are quite specialized (e.g., stroke vs. MS vs. acute encephalopathy vs. optic neuritis). In the majority of cases, the specialist neurologist is better at reading the MRI of their target conditions than the general radiologist. This is handled by the neurologist phoning the radiologist and guiding them to re-work their findings. The vast majority of AI in software is in radiology. Decades ago radiology was among the first practice groups to fall under private equity patterns because of the cost of the machines. In some cases, specific radiology groups develop protectable expertise around some protocols (i.e., how to get the machine to give better data), but long machine lifecycle creates opportunities for technical one-upmanship. Knowing that the labeling quality is variable and the images are not entirely comparable, you realize the bulk of AI-radiology may be built on shifting sands, and amounts to workflow optimization. Worse, the effort could go into addressing what really plagues all providers: the unusability of their EHR's. But that's not particularly monetizable relative to critical diagnostics, and harder than production AI for MRI.