Digital Pathology and Whole Slide Imaging: What Changes Before AI
Whole slide imaging creates the digital specimen that pathology AI needs. The scanner is only one part of the clinical system, and digitization is not itself AI.
AI by Specialty
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.
What this predicts
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.
Key Points
- AI distribution across specialties tracks data infrastructure, not clinical need
- Radiology dominates because it digitized and standardized thirty years ago
- Pathology lags because most laboratories still read glass slides
- Specialties with no standardized digital data have almost no authorized AI
- Digital infrastructure adoption is the leading indicator for where AI arrives next
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