  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.
