Introduction/Background: Pathologists spend substantial time on fragmented digital work beyond slide review, including email triage, scheduling, document retrieval, follow-up communication, and case-related information synthesis. This operational burden increases inefficiency and context switching. OpenClaw, an agentic workflow environment, offers a practical way to unify daily administrative tasks with pathology-facing knowledge work in one interface.

Methods/Design: This session showcases OpenClaw across two connected domains: (1) routine productivity workflows such as inbox management, scheduling, reminders, and meeting preparation, and (2) pathology diagnostic workflow support, including structured case summarization, retrieval of prior materials, checklist-based sign-out support, draft report organization, and differential diagnosis assistance under pathologist oversight. Real-world and simulated pathology scenarios are used to demonstrate reproducible workflows.

Results: Representative use cases show that OpenClaw can reduce manual task switching, centralize fragmented information, and accelerate common professional tasks. In pathology-facing scenarios, its greatest value is as a supervised copilot for organizing context, surfacing relevant information, and supporting documentation rather than replacing medical judgment. Key lessons include boundary setting, verification, auditability, and explicit human-in-the-loop review.

Conclusion/Discussion: OpenClaw may bridge general-purpose agentic AI and real-world pathology operations. Thoughtful implementation can improve daily efficiency, reduce cognitive overhead, and support safer diagnostic workflows while preserving pathologist control. This showcase provides an actionable roadmap for adopting agentic assistants in pathology practice.

Learning Objectives:

1. Describe practical uses of OpenClaw for email, scheduling, and task management in pathology practice.
2. Explain how agentic workflows can support case preparation, report drafting, and diagnostic organization.
3. Identify safeguards needed for human oversight, verification, and responsible clinical deployment.

Cytopathology demands multiplane whole slide imaging for three-dimensional cell clusters, yet conventional z-stacking imposes disproportionate scan time and storage costs. Realtime Extended Focus (Realtime EF) merges focal planes in a single pass, potentially resolving this tradeoff. We benchmarked the Vieworks LH210 (Realtime EF) against 3DHistech FLASH using pathologist evaluation and dual automated quality control (QC) algorithms.Twelve cytopathology cases (eight organ sites; conventional smear and liquid-based cytology) were scanned at 40× under four conditions: 1-layer and 3-layer acquisition on each platform. Three blinded raters scored 144 randomised images across six quality domains on a 3-point Likert scale (Friedman test; Fleiss' κ). Automated QC used two parallel pipelines: GrandQC (EfficientNet-B0 UNet++ segmentation; 7-class artifact mapping) and a Laplacian variance focus metric (512×512 tile analysis). Scan time, file size, and quality-adjusted efficiency ratios were recorded.No platform differences emerged across all six domains for any specimen type (all post-hoc p > 0.05; Fleiss' κ 0.61-0.78). GrandQC detected lower artifact burden in LH210 images (18.1% vs. 29.7%; p = 0.005), especially out-of-focus regions (0.42% vs. 2.16%). Critically, GrandQC misclassified 3D cytology clusters as pen markings-domain shift from histopathology training-highlighting the need for cytology-specific model development. Laplacian and GrandQC were weakly correlated (r = −0.24), confirming complementary quality dimensions. LH210 achieved equivalent image quality at one-third the scan time (3.0 vs. 14.4 min) and file size (0.21 vs. 0.62 GB).LH210 Realtime EF delivers validated cytomorphological quality with threefold efficiency gains. Dual automated QC confirms objective artifact advantages while revealing a critical gap: histopathology-trained AI models require cytology-specific adaptation before routine deployment.

Learning Objectives:

1. Compare image quality and scanning efficiency of Realtime EF versus z-stacking across diverse cytopathology specimen types.
2. Assess GrandQC deep learning and Laplacian variance as dual automated QC tools for WSI benchmarking in digital cytopathology.
3. Reveal readiness gaps in histopathology-trained AI models for cytology and define a cytology-specific QC development agenda.