Deployment of AI-driven automated quality control of whole-slide images in a large tertiary cancer center

Introduction: Quality control (QC) remains a major challenge in digital histopathology, as artifacts introduced during slide preparation and whole-slide imaging (WSI) can compromise diagnostic accuracy. Automated QC has emerged as a potential solution to the scalability and variability limitations of manual review in digital pathology workflows. However, there is limited evidence describing real-world, enterprise-scale implementation of automated QC systems within high-throughput clinical environments. Materials and methods: We evaluated the clinical implementation of a commercially available, AI-based automated QC platform (AIRAQc) within a large digital pathology lab. Feasibility testing included 60 histopathology slides scanned across 3 WSI platforms. Following enterprise integration, a retrospective operational analysis was performed on 94,995 WSIs generated over 1 month across 10 subspecialty services and multiple stain types. System performance, artifact prevalence, processing latency, reproducibility, and scalability were assessed using structured data exports, statistical analyses, and controlled load-testing scenarios. Results: Artifact detection demonstrated high reproducibility across scanner platforms, with tissue fold detection showing >97% concordance and air bubble detection exceeding 98% concordance. Scanning-related artifacts, including missing tissue and blurred regions, varied by scanner model but were consistently identified. Across the clinical deployment, the mean analysis time was 17 s/image, with no analysis failures observed during load testing. Mean total artifact burden/image was 1.91%, with most images meeting predefined QC thresholds. The system maintained stable performance under sustained high-throughput conditions, supporting daily volumes exceeding 6000 slides without workflow disruption. Conclusions: This study demonstrates the feasible deployment of an AI-based QC framework within a large-scale, multi-vendor clinical digital pathology environment. The QC framework enabled consistent assessment of routine WSIs with low per-image latency and sustained high-throughput scanning without workflow disruption. Consistent application of QC thresholds across a multi-instrument infrastructure reduced reliance on manual review and supports the integration of automated QC as a core component of contemporary digital pathology workflows.