A printed conference program will not be provided onsite. However, you can download and print the full Pathology Visions 2026 agenda here.
PV26 Schedule of Events
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:
- Compare how payment is structured for AI use cases in pathology, ophthalmology, cardiology, and radiology.
- Distinguish digitization reimbursement from payment for AI-influenced clinical services.
- Apply a framework to assess pathology AI readiness for community-practice deployment.
