@Article{jansen2022journal,
  author   = {Christoph Jansen and Björn Lindequist and Klaus Strohmenger and Daniel Romberg and Tobias Küster and Nick Weiss and Michael Franz and Lars Ole Schwen and Theodore Evans and André Homeyer and Norman Zerbe},
  title    = {The vendor-agnostic {EMPAIA} platform for integrating {AI} applications into digital pathology infrastructures},
  journal  = {Future Generation Computer Systems},
  year     = {2022},
  issn     = {0167-739X},
  abstract = {Automated image analysis and artificial intelligence (AI) are becoming increasingly common in digital pathology software. 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 is developing an open and decentralized platform allowing AI-based apps of different vendors to be integrated with existing clinical IT infrastructures. For this purpose, we defined, analyzed, and prioritized relevant use cases and identified requirements for an open platform to support these use cases. We then designed the platform architecture described here to meet these requirements based on web technologies. For all platform services open source reference implementations are available, that are used by developers of AI apps as an integration target. Developers of compatible clinical systems can either use and integrate components of the reference implementation or directly implement the interfaces as per specification, allowing apps to run in their clinical environment. Pathology laboratories can use both on-premises and cloud deployments of the platform. Apps can be obtained via a central marketplace so that pathologists can use them in their daily workflow. An adoption of this platform will enable interoperability among different existing digital pathology software systems. This reduces integration efforts for software vendors, while users will benefit from a wider variety of tools and a quicker availability of new and innovative methods. Ultimately, the platform will reduce barriers to market entry for AI vendors and provide pathologists with access to advanced AI tools.},
  doi      = {https://doi.org/10.1016/j.future.2022.10.025},
  keywords = {Computational pathology, Artificial intelligence, Applications, API, Interoperability, Digital platform},
  url      = {https://www.sciencedirect.com/science/article/pii/S0167739X22003405},
}