Introduction/Background: Adoption of digital pathology (DP) is accelerating as institutions transition to fully or partially digital workflows. DP underpins augmented intelligence (AI) applications that can improve diagnostic accuracy, efficiency, and patient care. However, implementation goes beyond selecting scanners and monitors. DP and AI must operate within a broader ecosystem that includes the electronic health record (EHR), laboratory information systems (LIS), pathology imaging management systems (PIMS), and cloud infrastructure. Navigating this interconnected environment is a key challenge for pathology leaders, and the importance of seamless enterprise and cloud integration is often underestimated.

Methods/Design: This 60-minute moderated panel will feature experts in enterprise informatics, imaging systems, and pathologists experienced in DP and AI implementation. Discussion topics include the current ecosystem landscape, real-world lessons learned, barriers and opportunities for integration, and strategies to strengthen collaboration across the pathology community. Panelists and moderators will meet virtually twice before PV26 to align content and structure. Approximately 20 minutes will be dedicated to audience Q&A to encourage interaction.Expected

Outcomes/Results: The session will establish a foundation for continued dialogue on the DP and AI ecosystem. Key insights will be summarized in a blog post for the Digital Pathology Association (DPA) website to support ongoing knowledge sharing.

Conclusions/Discussion: DP and AI require coordinated integration within a complex healthcare ecosystem. Scanner selection is only one step; success depends on interoperability, workflow integration, and enterprise alignment. Continued collaboration across stakeholders will be essential to fully realize the potential of this evolving field.

Learning Objectives:

1. Be familiar with the current landscape of the DP and AI ecosystem
2. Learn experiences, key barriers, and solutions to real-world implementation
3. Brainstorm strategies for collaborative progress across the pathology community

Traditional manual scoring of immunohistochemistry (IHC) faces limitations in evaluating novel biomarkers for precision oncology. Conventional companion diagnostics rely on subjective, ordinal scoring systems (e.g., 0, 1+, 2+, 3) which may limit their ability to capture more complex biological mechanisms. Conversely, computational pathology AI tools capture objective, quantitative measurements, many that cannot be achieved by eye alone. Because computational pathology scores cannot be predicted manually, transitioning from ordinal frameworks to automated, continuous mathematical models is a difficult paradigm shift for pathologists to trust. To establish the analytical evidence required to build trust, a Roche-sponsored study evaluated the reproducibility of the TROP2 (EPR20043) NSCLC RUO algorithm across 12 real-world laboratories in eight countries. To evaluate the pathologist workflow, centrally stained slides were provided to determine concordance of digital results, and unstained slides were provided to assess full device concordance. The former achieved an overall inter-site agreement of 100% and the latter workflow achieved an overall inter-site agreement rate of 94.1%, which increased to 99.8% when excluding borderline cases near the established threshold. The workshop will conclude with an interactive Q&A forum to discuss how the results may impact the future of AI-driven precision oncology.

Learning Objectives:

1. Describe the biological mechanism of TROP2 normalized membrane ratio (NMR) in NSCLC
2. Discuss analytical reproducibility & concordance rates of automated TROP2 NMR scoring across 12 diverse international labs
3. Understand pathologist & lab roles and how this study serves as a practical guide for computational pathology deployment