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 11 AI-based image analysis apps from 6 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.

过去十年间，病理学领域中的人工智能方法取得了显著进展，然而由于技术和监管方面的困难以及缺乏标准化接口，其在临床实践中的整合进展缓慢。EMPAIA 倡议以开放和供应商中立的方式应对这些挑战，并在病理学人工智能生态系统中集成了病理学家、计算机科学家和行业的各方利益相关者。通过密切合作，我们开发了技术互操作标准、AI 测试和产品开发的建议以及可解释性方法。我们实现了模块化开源的 EMPAIA 平台，并成功整合了来自 6 个不同供应商的 11 个基于 AI 的图像分析应用程序，展示了不同应用程序如何使用单一的标准化接口。我们在欧洲和亚洲的 14 个不同病理实验室中优先考虑了需求，并评估了 AI 在真实临床环境中的应用。除了技术发展，我们还创建了一个论坛，让所有利益相关者分享关于数字病理学和人工智能的信息和经验。商业、临床和学术利益相关者现在可以采用 EMPAIA 的公共开源接口，为大规模标准化和流程优化提供了独特机会。还需要进一步努力，以在日常实验室使用中有效广泛地建立人工智能辅助。为此，我们建立了一个可持续的非营利组织 EMPAIA 国际，以继续标准化工作，并支持广泛的实施和倡导 AI 辅助数字病理学的未来。