5 Key Takeaways

• 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.