Democratizing AI in Healthcare with Open Medical Inference (OMI): Protocols, Data Exchange, and AI Integration
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
- Rickmer Braren (ORCID: https://orcid.org/0000-0001-6039-6957)
- Christian Fegeler (ORCID: https://orcid.org/0000-0001-5581-7155)
- Timo Apfelbacher (ORCID: https://orcid.org/0009-0003-0149-695X)
- P. Wilhelm Werner
- Martin Boeker (ORCID: https://orcid.org/0000-0003-2972-2042)
- Lucas Giuliani Scherer (ORCID: https://orcid.org/0000-0003-2912-6398)
- Obioma Pelka (ORCID: https://orcid.org/0000-0001-5156-4429)
- Hans‐Ulrich Prokosch (ORCID: https://orcid.org/0000-0001-6200-753X)
- Stefan Sigle (ORCID: https://orcid.org/0000-0001-7475-4583)
- Andreas Bucher (ORCID: https://orcid.org/0000-0001-9194-5955)
- Matthias May (ORCID: https://orcid.org/0000-0002-2540-850X)
- Felix Nensa (ORCID: https://orcid.org/0000-0002-5811-7100)
- Tobias Susetzky
- Simon T. Schweizer (ORCID: https://orcid.org/0000-0002-0888-4301)
- Alexa Iancu
- Dmitrii Seletkov
- Jan Horst Eil (ORCID: https://orcid.org/0009-0005-9879-5722)
- Nicolas Andreas Kamzol
Institutions
- Goethe University Frankfurt (DE)
- Friedrich-Alexander-Universität Erlangen-Nürnberg (DE)
- TUM Klinikum (DE)
- Heilbronn University (DE)
- Universitätsklinikum Erlangen (DE)
- Center for Personalized Cancer Treatment (NL)
- Essen University Hospital (DE)
- University Hospital Frankfurt (DE)
- Artificial Intelligence in Medicine (Canada) (CA)
- Technical University of Munich (DE)
Publication Details
- Journal
- RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren
- Published
- 2025-09-29
- DOI
- https://doi.org/10.1055/a-2651-6653
- Citations
- 1
- Primary Topic
- Big Data and Business Intelligence
- Type
- article
- Field-Weighted Citation Impact
- 2.28
Funders
- Bundesministerium für Bildung und Forschung