Democratizing AI in Healthcare with Open Medical Inference (OMI): Protocols, Data Exchange, and AI Integration.

Journal: RoFo : Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin
Published Date: Sep 29, 2025

Abstract

The integration of artificial intelligence (AI) into healthcare is transforming clinical decision-making, patient outcomes, and workflows. AI inference, applying trained models to new data, is central to this evolution, with cloud-based infrastructures enabling scalable AI deployment. The Open Medical Inference (OMI) platform democratizes AI access through open protocols and standardized data formats for seamless, interoperable healthcare data exchange. By integrating standards like FHIR and DICOMweb, OMI ensures interoperability between healthcare institutions and AI services while fostering ethical AI use through a governance framework addressing privacy, transparency, and fairness.OMI's implementation is structured into work packages, each addressing technical and ethical aspects. These include expanding the Medical Informatics Initiative (MII) Core Dataset for medical imaging, developing infrastructure for AI inference, and creating an open-source DICOMweb adapter for legacy systems. Standardized data formats ensure interoperability, while the AI Governance Framework promotes trust and responsible AI use.The project aims to establish an interoperable AI network across healthcare institutions, connecting existing infrastructures and AI services to enhance clinical outcomes. · OMI develops open protocols and standardized data formats for seamless healthcare data exchange.. · Integration with FHIR and DICOMweb ensures interoperability between healthcare systems and AI services.. · A governance framework addresses privacy, transparency, and fairness in AI usage.. · Work packages focus on expanding datasets, creating infrastructure, and enabling legacy system integration.. · The project aims to create a scalable, secure, and interoperable AI network in healthcare.. · Pelka O, Sigle S, Werner P et al. Democratizing AI in Healthcare with Open Medical Inference (OMI): Protocols, Data Exchange, and AI Integration. Rofo 2026; 198: 173-184.

Authors

  • Obioma Pelka
    Department of Computer Science, University of Applied Sciences and Arts Dortmund (FHDO), Dortmund, NRW Germany.
  • Stefan Sigle
    MOLIT Institute, Heilbronn, Germany.
  • Patrick Werner
    MOLIT Institute gGmbH, Heilbronn, Germany.
  • Simon Tobias Schweizer
    Faculty of Informatics, Heilbronn University of Applied Sciences, Heilbronn, Germany.
  • Alexa Iancu
    Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Wetterkreuz 15, 91058 Erlangen, Germany.
  • Lucas Scherer
    Medical Centre for Information and Communication Technology, Erlangen University Hospital, Erlangen, Germany.
  • Nicolas Andreas Kamzol
    Institute of Artificial intelligence in Medicine, University Hospital Essen, Essen, Germany.
  • Jan Horst Eil
    Institute for Artificial Intelligence in Medicine (IKIM), University Hospital Essen, Essen, Germany.
  • Timo Apfelbacher
    Department of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
  • Dmitrii Seletkov
    Institute of Diagnostic and Interventional Radiology, Technical University of Munich School of Medicine, Munich, Germany. [email protected].
  • Tobias Susetzky
    Institute of Diagnostic and Interventional Radiology, Technical University of Munich, Munich, Germany.
  • Matthias Stefan May
    Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Erlangen, Germany.
  • Andreas Michael Bucher
    University Hospital, Institute for Diagnostic and Interventional Radiology, Goethe University Frankfurt Faculty 16 Medicine, Frankfurt am Main, Germany.
  • Christian Fegeler
    MOLIT Institute, Heilbronn, Germany.
  • Martin Boeker
    Institute for Medical Biometry and Statistics, Medical Center - University of Freiburg, Faculty of Medicine, Stefan-Meier-Str. 26, Freiburg i. Br., 79104, Germany. [email protected].
  • Rickmer Braren
    Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, Munich, Germany.
  • Hans-Ulrich Prokosch
    Institute for Medical Informatics, University Erlangen-Nuremberg, Erlangen, Germany; Center for Medical Information and Communication, Erlangen University Hospital, Erlangen, Germany.
  • Felix Nensa
    Institute for AI in Medicine (IKIM), University Hospital Essen, 45131 Essen, Germany.

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