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.

Introduction: The Joint Commission and Coalition for Health AI released Responsible Use of AI in Healthcare (RUAIH) guidance in September 2025, defining seven elements for responsible clinical AI deployment. The extent to which pathology AI publications document these governance elements has not been measured.

Methods: We searched PubMed (2019-2026) using ('computational pathology' OR 'digital pathology') AND (artificial intelligence OR deep learning OR machine learning), yielding 1,460 publications across 368 journals. We operationalized the seven RUAIH elements into the Governance Readiness Score (GRS): a 0-2 anchored rubric per element (maximum 14) built on explicit textual indicators and rubric-aligned keyword ontologies. A scripted GPT-4o pipeline with a fixed rubric prompt scored title/abstract records for all 1,460 publications (interim analysis); full-text re-scoring of the PubMed Central subset (n=942) is underway. A blinded 30-paper subset was independently rescored by a second large language model (Pearson r=0.96; 90% within 1 point). A board-certified pathologist blindly scored a stratified 10-paper subset (5 highest, 5 zero) with substantial expert-model agreement (Cohen's κ=0.79 expert-vs-primary; κ=0.80 expert-vs-secondary; Landis-Koch).

Results: Mean GRS was 0.42/14 (3%). 940 of 1,460 publications (64.4%) scored zero on all seven elements; the maximum observed was 8/14. Only the risk and bias assessment showed substantive engagement (28.8% of publications scored>=1); safety event reporting was nearly universally absent (99.9% zero). No abstract referenced an AI model card.

Conclusions: Pathology AI abstracts rarely document governance scaffolding expected for responsible clinical deployment under RUAIH. The most actionable gaps were safety event reporting and model-card-level transparency. Governance-aware publication standards are needed before clinical deployment scales.

Learning Objectives: 

1. Describe seven RUAIH elements relevant to safe clinical deployment of pathology AI tools.
2. Apply the Governance Readiness Score to evaluate pathology AI publications for deployment readiness.
3. Identify governance documentation gaps relevant to authors, reviewers, and health systems.