The EMPAIA Platform: Vendor-neutral integration of AI applications into digital pathology infrastructures

Abstract

Automated image analysis and artificial intelligence (AI) are a growing market in digital pathology. While various proprietary pathology systems exist, there are no fully vendor-agnostic integration approaches for AI apps. This makes it difficult for vendors of AI solutions to integrate their products into the multitude of non-standard software systems in pathology. The EMPAIA Consortium (EcosysteM for Pathology Diagnostics with AI Assistance) develops an open and decentralized platform allowing AI-based apps of different vendors to be integrated with existing lab IT infrastructures. This is intended to lower the barriers to entry for AI vendors and provide pathologists with access to advanced AI tools. The EMPAIA platform is based on web technologies that can be deployed both on-premises and in the cloud. There are open-source reference implementations for core platform services that can be integrated with or replaced by proprietary alternatives as long as they conform to open API specifications. Apps can be obtained through a central marketplace so pathologists can use them in their daily workflow. In this paper, we provide an overview of the EMPAIA platform architecture. We identify critical use cases and requirements for AI-based software platforms in pathology and explain how these are fulfilled by the EMPAIA platform. Finally, we evaluate the efficiency of routing image data through the platform.