OVERVIEW
AI is transforming pathology, but building and deploying impactful models remains a challenge—particularly for organizations with limited or fragmented technical resources. Common
barriers like disconnected data, complex infrastructure, unclear use cases, and the need for specialized expertise can stall even the most promising efforts.
In this interactive workshop for scientists, lab leaders, and technically curious professionals, Proscia won’t just show you what it takes to bring AI to life in a pathology setting. You’ll actively participate in building AI yourself.
We’ll begin by demystifying the core elements of an AI-ready digital pathology ecosystem. Topics will include harmonizing image data, organizing structured metadata, and building and running AI models. Then, we’ll move into a hands-on session where participants will walk through a simple yet powerful end-to-end AI use case, guided by Proscia’s AI and engineering teams and powered by the newly released Concentriq Embeddings solution.
This workshop is intended for participants with at least minimal familiarity with Python. You don’t need to be a full-fledged developer, but some comfort navigating a Python notebook will help you get the most out of the session.
Whether you’re a scientist, digital pathology lead, or technical stakeholder, you’ll leave with a practical understanding of how AI can be built, tested, and integrated into pathology workflows faster than you might expect. You’ll also explore how tools like Embeddings, open APIs, and scalable data pipelines can accelerate your journey.
We’ll close with Q&A and open office hours to help take you from exploration to execution—equipped with the ideas, tools, and next steps to move forward.
Registration is subject to final approval from Proscia.
Digital Pathology & AI Congress: USA
Utilizing Digital Pathology & AI to advance pathology practice, enable enhanced patient care and further drug discovery.
Location:
Philadelphia, USA
Date:
5 th Jun 2025 to 6 th Jun 2025
Field:
Medical Imaging
Subject:
Digital Pathology
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