How computational pathology and AI are changing slide analysis, cancer grading, and diagnostic accuracy.
Computational pathology uses AI to analyze digital pathology slides at scale. Models can identify cancer cells, grade tumors, and detect patterns that support more consistent and faster diagnoses.
Pathology should have been an obvious early target for medical AI and was not, for an entirely unglamorous reason: the slides were not digital.
Radiology handed machine learning a standardized digital corpus. Pathology handed it a drawer of glass. Whole slide imaging had to be developed, validated, and authorized for primary diagnosis before any model could be trained, and scanning remains expensive with storage requirements measured in gigabytes per slide.
That dependency is the whole explanation for why the FDA device list contains hundreds of radiology entries and, in pathology, essentially one.
Digitizing a pathology laboratory costs real money and, on its own, mostly reproduces what a microscope already did. The investment case rests almost entirely on what the AI layer adds later, which makes it a harder internal sell than PACS ever was in radiology.
Authorized pathology AI is currently a single adjunct device for a single tissue type. The research literature is far ahead of the regulatory record, with promising work on grading, biomarker prediction from morphology, and prognostic modeling ... almost none of which is authorized. Anyone reading that literature should keep the gap between "published" and "cleared" firmly in view, and note that whole slide imaging is a prerequisite that most laboratories have not yet bought.
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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.
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.
What FDA clearance means for AI medical devices, how the regulatory pathways work, and what clinicians need to understand before deploying FDA-cleared AI tools.
Triage, detection, characterization, and autonomous diagnosis make different clinical claims. Here is the evidence question that belongs to each one.