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Joining Forces for Pathology Diagnostics with AI Assistance: The EMPAIA Initiative

Norman Zerbe, Lars Ole Schwen, Christian Geißler, Katja Wiesemann, Tom Bisson, Peter Boor, Rita Carvalho, Michael Franz, Christoph Jansen, Tim-Rasmus Kiehl, Björn Lindequist, Nora Charlotte Pohlan, Sarah Schmell, Klaus Strohmenger, Falk Zakrzewski, Markus Plass, Michael Takla, Tobias Küster, André Homeyer, Peter Hufnagl

TL;DR

The paper addresses the barrier to widespread AI adoption in pathology by proposing open, vendor-neutral standards and a modular platform to integrate diverse AI apps into clinical workflows. It presents the EMPAIA App Interface, an open-source platform, and a validation ecosystem, demonstrated through the integration of 14 apps from eight vendors across 16 reference centers, with real-world feedback and regulatory guidance. A strong emphasis is placed on explainability tailored to varying stakeholders, along with a formalized validation service to support regulatory approval. The work also outlines knowledge transfer through EMPAIA Academy and public dissemination, and proposes a sustainable, non-profit organizational path to drive ongoing standardization and broad adoption of AI-assisted digital pathology, while acknowledging remaining regulatory, reimbursement, and digitization challenges.

Abstract

Over the past decade, artificial intelligence (AI) methods in pathology have advanced substantially. However, integration into routine clinical practice has been slow due to numerous challenges, including technical and regulatory hurdles in translating research results into clinical diagnostic products and the lack of standardized interfaces. The open and vendor-neutral EMPAIA initiative addresses these challenges. Here, we provide an overview of EMPAIA's achievements and lessons learned. EMPAIA integrates various stakeholders of the pathology AI ecosystem, i.e., pathologists, computer scientists, and industry. In close collaboration, we developed technical interoperability standards, recommendations for AI testing and product development, and explainability methods. We implemented the modular and open-source EMPAIA platform and successfully integrated 14 AI-based image analysis apps from 8 different vendors, demonstrating how different apps can use a single standardized interface. We prioritized requirements and evaluated the use of AI in real clinical settings with 14 different pathology laboratories in Europe and Asia. In addition to technical developments, we created a forum for all stakeholders to share information and experiences on digital pathology and AI. Commercial, clinical, and academic stakeholders can now adopt EMPAIA's common open-source interfaces, providing a unique opportunity for large-scale standardization and streamlining of processes. Further efforts are needed to effectively and broadly establish AI assistance in routine laboratory use. To this end, a sustainable infrastructure, the non-profit association EMPAIA International, has been established to continue standardization and support broad implementation and advocacy for an AI-assisted digital pathology future.

Joining Forces for Pathology Diagnostics with AI Assistance: The EMPAIA Initiative

TL;DR

The paper addresses the barrier to widespread AI adoption in pathology by proposing open, vendor-neutral standards and a modular platform to integrate diverse AI apps into clinical workflows. It presents the EMPAIA App Interface, an open-source platform, and a validation ecosystem, demonstrated through the integration of 14 apps from eight vendors across 16 reference centers, with real-world feedback and regulatory guidance. A strong emphasis is placed on explainability tailored to varying stakeholders, along with a formalized validation service to support regulatory approval. The work also outlines knowledge transfer through EMPAIA Academy and public dissemination, and proposes a sustainable, non-profit organizational path to drive ongoing standardization and broad adoption of AI-assisted digital pathology, while acknowledging remaining regulatory, reimbursement, and digitization challenges.

Abstract

Over the past decade, artificial intelligence (AI) methods in pathology have advanced substantially. However, integration into routine clinical practice has been slow due to numerous challenges, including technical and regulatory hurdles in translating research results into clinical diagnostic products and the lack of standardized interfaces. The open and vendor-neutral EMPAIA initiative addresses these challenges. Here, we provide an overview of EMPAIA's achievements and lessons learned. EMPAIA integrates various stakeholders of the pathology AI ecosystem, i.e., pathologists, computer scientists, and industry. In close collaboration, we developed technical interoperability standards, recommendations for AI testing and product development, and explainability methods. We implemented the modular and open-source EMPAIA platform and successfully integrated 14 AI-based image analysis apps from 8 different vendors, demonstrating how different apps can use a single standardized interface. We prioritized requirements and evaluated the use of AI in real clinical settings with 14 different pathology laboratories in Europe and Asia. In addition to technical developments, we created a forum for all stakeholders to share information and experiences on digital pathology and AI. Commercial, clinical, and academic stakeholders can now adopt EMPAIA's common open-source interfaces, providing a unique opportunity for large-scale standardization and streamlining of processes. Further efforts are needed to effectively and broadly establish AI assistance in routine laboratory use. To this end, a sustainable infrastructure, the non-profit association EMPAIA International, has been established to continue standardization and support broad implementation and advocacy for an AI-assisted digital pathology future.
Paper Structure (18 sections, 3 figures, 1 table)

This paper contains 18 sections, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Technical Developments and Evaluation
  3. Standardization
  4. The EMPAIA Platform
  5. Lessons Learned
  6. Perspective
  7. Reference Centers
  8. Platform Deployment and Usage
  9. Lessons Learned
  10. Explainability
  11. Regulatory Perspective
  12. Communication and Knowledge Transfer
  13. Public Relations
  14. EMPAIA Academy
  15. Discussion
  16. ...and 3 more sections

Figures (3)

  • Figure 1: Locations of national and international EMPAIA partners. (World map adapted from Wikimedia Commons WorldMap).
  • Figure 2: EMPAIA Platform architecture. Simplified diagram of the EMPAIA Platform architecture, showing the main APIs serving as abstractions over clinical system software components.
  • Figure 3: XAI Approaches. Overview of different XAI approaches, the level they address and the project deliverables that have been affected or designed to support them. Local approaches provide explanations during usage of AI applications and directly address pathologists. Global approaches are supplements of AI applications that help decide whether they should be acquired in general.