From FDA clearance to deployable workflow: AI readiness in digital pathology

Background: In pathology, recent Category III digitization add-on codes created reporting pathways for digitization, but did not establish payment for algorithmic interpretation. Radiology dominates the FDA AI-enabled device landscape, and pathology has far fewer authorized products. A framework is needed to assess whether an AI product is valid, operationally adoptable, and economically translatable for community practice.

Methods: PATH (Pathology AI Translation Heuristic) is a three-domain framework drawn from FDA, CMS/CPT, and implementation sources: clinical validity (design, population, performance), operational adoptability (workflow, validation burden, IT/training), and economic translatability (regulatory pathway, payment mechanism, attachment of payment to AI-influenced clinical service vs infrastructure). PATH was applied through a comparative review of pathology, ophthalmology, cardiology, and radiology, using 2023-2026 sources(Figure 1).

Results: Authorization did not reliably predict payment maturity or deployability. Ophthalmology demonstrated the clearest payment attachment under CPT 92229 for autonomous detection of diabetic retinopathy. Cardiology demonstrated service-line payment in CT-derived fractional flow reserve (FFR-CT). Radiology illustrated that authorization volume alone does not ensure reimbursement. In pathology, coding progress concentrated at the digitization layer (0751T-0763T, 0827T-0856T) while algorithmic interpretation lacked service-level payment. Greatest friction was in community deployment, where validation, workflow, IT, and uncertain payment converge.

Conclusion: Single-axis evaluation around clearance or accuracy misses the adoption bottleneck. Cross-specialty comparison suggests deployability improves when payment attaches to the AI-influenced clinical service rather than only enabling infrastructure. PATH may support procurement, validation, and implementation decisions for pathologists, labs, and health systems.

Learning Objectives:

1. Compare how payment is structured for AI use cases in pathology, ophthalmology, cardiology, and radiology.
2. Distinguish digitization reimbursement from payment for AI-influenced clinical services.
3. Apply a framework to assess pathology AI readiness for community-practice deployment.