Mini Summary:
Structured pathology reporting is quietly laying the
groundwork for AI-generated reports. We argue that this will transform digital
pathology from image digitization into a productivity engine—and triggering its
long-awaited billion-dollar hockey stick for digital pathology. Find the white paper here.
Full Summary:
Digital
pathology has largely been evaluated at the front end of the workflow:
converting glass slides into digital images and selectively adding algorithms that detect,
measure, or classify features. This paper argues that the more consequential
endpoint lies at the opposite end—when digital systems can assemble much of the
finished pathology report.
The thesis builds on two six-level frameworks.
- Fryback and Thornbury asked whether diagnostic technologies ultimately improve clinical decisions, outcomes, and societal value.
- Ellis and Srigley described the evolution of pathology reporting from narrative prose to structured, computable, interoperable data. AI now connects these two traditions. Structured pathology data can be transformed into draft reports, while image-analysis systems increasingly generate the findings that populate those reports.
A related proposal from Lennerz and colleagues envisions the pathologist as a “diagnostic architect,” integrating morphology, genomics, imaging, clinical information, and computational outputs. AI-assisted report generation could create the capacity for that higher-order role.
The paper therefore proposes an emergent “seventh level”: not another reporting standard, but an economic inflection point where structured data, AI, and workflow integration transform digital pathology into a major productivity engine. Find the white paper here.
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See PDF in cloud here. |
