AI-Driven Advancements in Biomarker Analysis

Proscia Concentriq supports automated AI-driven biomarker analysis workflows that surface algorithm outputs directly within the digital slide review stream through integrated scoring modules and centralized tools. These workflows are designed to standardize quantification across teams while maintaining pathologist review and sign-out within routine diagnostic practice.

The system provides automated visual overlays, batch-scoring capabilities, and structured result fields that reduce manual measurement steps and align outputs with commonly used scoring frameworks. Vendor-reported evaluations and early-adopter use cases describe efficiency gains such as reduced hands-on time and streamlined workflows, though performance and impact depend on local implementation and validation.

These capabilities—including automated pre-screening, prioritized case routing, and algorithm-based flagging—are intended to support triage and reduce sources of inter-observer variability in selected use cases. Commercial AI modules apply consistent scoring logic across heterogeneous specimens, and the platform’s modular architecture allows third-party algorithms to operate within the same review pipeline, subject to local governance and validation.

Current applications include AI modules for PD-L1, HER2, Ki-67, and ER/PR analysis. Supported analytic tasks may include automated cell detection and counting, membrane assessment, tumor proportion scoring (TPS/CPS), and H-score calculations. Availability, validated use, and regulatory clearance vary by biomarker, assay clone, specimen type, indication, and region, with more limited and assay-specific support for certain preparations such as cytology cell blocks.