Democratizing AI in Healthcare with Open Medical Inference (OMI): Protocols, Data Exchange, and AI Integration.
Authors
Affiliations (13)
Affiliations (13)
- Institute of Artificial intelligence in Medicine, University Hospital Essen, Essen, Germany.
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany.
- Knowledge Management, MOLIT Institute for Personalized Medicine, Heilbronn, Germany.
- Faculty of Informatics, Heilbronn University of Applied Sciences, Heilbronn, Germany.
- Medical Centre for Information and Communication Technology, Erlangen University Hospital, Erlangen, Germany.
- Department of Medical Informatics, Biometrics and Epidemiology, Medical Informatics, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany.
- Institute of Diagnostic and Interventional Radiology, Technical University of Munich, Munich, Germany.
- Radiology Clinic, Erlangen University Hospital, Erlangen, Germany.
- University Hospital, Institute for Diagnostic and Interventional Radiology, Goethe University Frankfurt Faculty 16 Medicine, Frankfurt am Main, Germany.
- Institute of Artificial Intelligence and Informatics in Medicine, Technical University of Munich School of Medicine, Munich, Germany.
- Institute of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technical University of Munich School of Medicine, Munich, Germany.
- German Cancer Consortium, a Partnership Between DKFZ and School of Medicine, Technical University of Munich, Munich, Germany.
- Institute for Artificial Intelligence in Medicine (IKIM), University Hospital Essen, Essen, Germany.
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 2025; DOI 10.1055/a-2651-6653.