Why Accurate Pathology AI Is Not Enough

New framework highlights the evidence needed to translate AI performance into better diagnostic decisions and patient outcomes

1

High diagnostic accuracy alone does not establish the clinical value of AI in digital pathology.
2

The authors propose a six-stage framework spanning tissue preparation, image acquisition, model performance, laboratory workflow, clinical decisions, and patient outcomes.
3

Differences in tissue processing, scanners, and laboratory procedures can affect image quality and AI performance, making external validation and local testing important.
4

In a study involving 28 pathology experts, incorrect AI recommendations overturned previously correct assessments in approximately 7% of consultations.
5

The conceptual framework has not been validated prospectively and calls for ongoing monitoring of AI performance after implementation.

1