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Over the past decade, artificial intelligence (AI) methods in pathology haveadvanced substantially. However, integration into routine clinical practice hasbeen slow due to numerous challenges, including technical and regulatoryhurdles in translating research results into clinical diagnostic products andthe lack of standardized interfaces. The open and vendor-neutral EMPAIAinitiative addresses these challenges. Here, we provide an overview of EMPAIA'sachievements and lessons learned. EMPAIA integrates various stakeholders of thepathology 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 explainabilitymethods. We implemented the modular and open-source EMPAIA platform andsuccessfully integrated 11 AI-based image analysis apps from 6 differentvendors, demonstrating how different apps can use a single standardizedinterface. We prioritized requirements and evaluated the use of AI in realclinical settings with 14 different pathology laboratories in Europe and Asia.In addition to technical developments, we created a forum for all stakeholdersto share information and experiences on digital pathology and AI. Commercial,clinical, and academic stakeholders can now adopt EMPAIA's common open-sourceinterfaces, providing a unique opportunity for large-scale standardization andstreamlining of processes. Further efforts are needed to effectively andbroadly establish AI assistance in routine laboratory use. To this end, asustainable infrastructure, the non-profit association EMPAIA International,has been established to continue standardization and support broadimplementation and advocacy for an AI-assisted digital pathology future.

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