Operationalizing AI in Pathology: Strategy, Governance, and Clinical Integration

As digital pathology and AI move from pilot projects to enterprise expectations, many institutions face a common challenge: how to translate innovation into safe, sustainable clinical practice. Participants will explore what it truly takes to operationalize computational pathology within a healthcare system—moving beyond algorithms to strategy, governance, validation, and long-term impact. This session addresses the growing need for structured, end-to-end frameworks that connect research, infrastructure, and regulatory rigor with real-world patient care.

Attendees will walk through the full AI lifecycle—from institutional vision and infrastructure design to model development, clinical validation, regulatory oversight, and operational deployment. Participants will gain practical insights into building AI programs that integrate whole slide image analysis into routine diagnostic workflows while maintaining quality, compliance, and patient safety. The session emphasizes how mature AI centers bridge innovation and clinical care without sacrificing transparency or accountability.

By the end of the session, participants will leave with a clearer roadmap for designing or scaling AI centers that are clinically meaningful, operationally sustainable, and aligned with evolving healthcare regulations.

This session is presented in collaboration with the Digital Pathology Association (DPA). 

Objectives

• Describe the organizational structure, technical infrastructure, and institutional requirements needed to establish and sustain Artificial Intelligence Centers within academic medical centers.
• Explain the clinical workflow of whole slide image analysis, including conventional and deep learning–based approaches, with emphasis on validation, deployment, and integration into routine diagnostic practice.
• Recognize the regulatory and governance considerations required for compliant, real-world AI implementation in healthcare settings.