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

The growing use of artificial intelligence in pathology is creating new opportunities and responsibilities for pathologists, including the evaluation of AI-assisted tools in clinical practice. Responsible implementation is essential to ensuring reliable performance, equitable outcomes, effective workflow integration, and appropriate clinical accountability. Existing regulatory and accreditation frameworks provide a strong foundation for AI-based tests, yet laboratories continue to navigate how best to operationalize these tools across the test lifecycle. As implementation approaches evolve, greater consistency can help strengthen confidence in AI-assisted pathology practice. This session presents the CAP AI Implementation Guide, a practical, step-by-step framework that translates established laboratory principles into a structured approach for implementing AI-based tests and promoting trustworthy use of AI in patient care.

Learning Objectives:

• Describe the major stages of the AI-based test implementation in pathology.
• Identify the clinical, operational, and technical considerations needed to determine whether an AI-based test is suitable for pathology practice.
• Apply key reporting principles that support transparency, trust, explainability, generalizability, and clinical actionability in AI-assisted pathology practice.

A pathologist does far more than sign out cases. Why should our digital platforms be built for only one part of the job?

In a single day, a pathologist may diagnose, consult, teach, collaborate, and do research…and leverage AI at every step. For decades, the microscope supported all these roles. But digitally, they’re often built as separate systems: one for clinical practice, others for consultation, education, or research.

What if you could connect them?

In this workshop, leaders from major digital pathology programs will show how they are using PathPresenter's scanner-agnostic, AI-agnostic, LIS-agnostic platform to build connected ecosystems with the pathologist back at the center. See how a single digital environment can support:

• Clinical sign-out: Integrated viewing, collaboration, annotation, and AI.
• Expert consultation: Connect subspecialty expertise across institutions and geography without moving glass.
• Education: Move beyond multiheaded microscopes and static PowerPoint to teach, collaborate, and share globally.
• Research: Find cases, connect images with data, collaborate, and create datasets for computational pathology.

Hear directly from the experts doing it:

Dr. Rajendra Singh
University of Pennsylvania | Founder, PathPresenter

Dr. Swati Suutarwar
The Ohio State University

Dr. Matthew Hanna
UPMC

Dr. Junya Fukuoka
Nagasaki University

Dr. Archana Shenoy
CCHMC

See how leading pathologists are using connected digital workflows today—and what they learned building them.

Attendees will leave with a practical understanding of how clinical care, consultation, education, research, and AI can exist within one connected digital ecosystem—and ideas they can take back to -their own institutions.