How AI is being applied across medical specialties including radiology, oncology, pathology, cardiology, neurology, and more.
Medical AI is distributed extremely unevenly across specialties, and the distribution is explained by data infrastructure rather than by clinical need.
Radiology has hundreds of authorized devices. Pathology has a handful. That gap is not because pathology is harder or matters less. Radiology has been digital since the 1990s, standardized through DICOM, with a mature predicate ecosystem to claim equivalence against. Most pathology laboratories still cut glass, and a model cannot analyze an image that was never created.
The pattern repeats. Cardiology has authorizations because ECG and echocardiography produce standardized digital signal. Ophthalmology has them because retinal photography is cheap, standardized, and produces a clear diagnostic question. Psychiatry has almost none, because the data is conversation and observation with no standard digital form and no clean reference standard.
Specialties acquire AI in the order their data becomes machine-readable, not in the order they need it. That makes digital infrastructure the leading indicator worth watching. Where whole slide imaging adoption goes, pathology AI follows about five years later.
Choose a whole slide imaging system as a complete diagnostic workflow, not as a scanner purchase. Compare clinical scope, image quality, validation, storage, integration, AI compatibility, and downtime planning.
Skin lesion classification became a computer vision benchmark with headline results. Almost none of it reached clearance, and the reason is in the training data.
Neurology has the clearest case in medicine for AI measured in minutes rather than accuracy points, and three very different problems that AI is being applied to.
Radiology has hundreds of authorized AI devices. Pathology has a handful. The gap is not about difficulty ... it is about whether the images exist at all.
Radiology has the highest concentration of FDA-cleared AI medical devices. Here is how AI is being used in practice in imaging departments today.