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

Obioma Pelka; Stefan Sigle; Patrick Werner; Simon Tobias Schweizer; Alexa Iancu; Lucas Scherer; Nicolas Andreas Kamzol; Jan Horst Eil; Timo Apfelbacher; Dmitrii Seletkov; Tobias Susetzky; Matthias Stefan May; Andreas Michael Bucher; Christian Fegeler; Martin Boeker; Rickmer Braren; Hans-Ulrich Prokosch; Felix Nensa

doi:10.1055/a-2651-6653

RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren, Inhaltsverzeichnis

Rofo
DOI: 10.1055/a-2651-6653

Review

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

Demokratisierung von KI im Gesundheitswesen mit Open Medical Inference (OMI): Protokolle, Datenaustausch und KI-Integration

Authors

Obioma Pelka

¹Institute of Artificial intelligence in Medicine, University Hospital Essen, Essen, Germany (Ringgold ID: RIN39081)

²Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany (Ringgold ID: RIN39081)
Stefan Sigle

³Knowledge Management, MOLIT Institute for Personalized Medicine, Heilbronn, Germany
Patrick Werner

³Knowledge Management, MOLIT Institute for Personalized Medicine, Heilbronn, Germany
Simon Tobias Schweizer

⁴Faculty of Informatics, Heilbronn University of Applied Sciences, Heilbronn, Germany (Ringgold ID: RIN40243)
Alexa Iancu

⁵Medical Centre for Information and Communication Technology, Erlangen University Hospital, Erlangen, Germany (Ringgold ID: RIN27168)
Lucas Scherer

⁵Medical Centre for Information and Communication Technology, Erlangen University Hospital, Erlangen, Germany (Ringgold ID: RIN27168)
Nicolas Andreas Kamzol

¹Institute of Artificial intelligence in Medicine, University Hospital Essen, Essen, Germany (Ringgold ID: RIN39081)
Jan Horst Eil

¹Institute of Artificial intelligence in Medicine, University Hospital Essen, Essen, Germany (Ringgold ID: RIN39081)
Timo Apfelbacher

⁶Department of Medical Informatics, Biometrics and Epidemiology, Medical Informatics, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany (Ringgold ID: RIN9171)
Dmitrii Seletkov

⁷Institute of Diagnostic and Interventional Radiology, Technical University of Munich, Munich, Germany (Ringgold ID: RIN9184)
Tobias Susetzky

⁷Institute of Diagnostic and Interventional Radiology, Technical University of Munich, Munich, Germany (Ringgold ID: RIN9184)
Matthias Stefan May

⁸Radiology Clinic, Erlangen University Hospital, Erlangen, Germany (Ringgold ID: RIN27168)
Andreas Michael Bucher

⁹University Hospital, Institute for Diagnostic and Interventional Radiology, Goethe University Frankfurt Faculty 16 Medicine, Frankfurt am Main, Germany (Ringgold ID: RIN144867)
Christian Fegeler

⁴Faculty of Informatics, Heilbronn University of Applied Sciences, Heilbronn, Germany (Ringgold ID: RIN40243)
Martin Boeker

¹⁰Institute of Artificial Intelligence and Informatics in Medicine, Technical University of Munich School of Medicine, Munich, Germany (Ringgold ID: RIN155892)
Rickmer Braren

¹¹Institute of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technical University of Munich School of Medicine, Munich, Germany (Ringgold ID: RIN155892)

¹²German Cancer Consortium, a Partnership Between DKFZ and School of Medicine, Technical University of Munich, Munich, Germany (Ringgold ID: RIN9184)
Hans-Ulrich Prokosch

⁶Department of Medical Informatics, Biometrics and Epidemiology, Medical Informatics, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany (Ringgold ID: RIN9171)
Felix Nensa

¹³Institute for Artificial Intelligence in Medicine (IKIM), University Hospital Essen, Essen, Germany (Ringgold ID: RIN39081)

²Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany (Ringgold ID: RIN39081)

Abstract

Background

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.

Method

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.

Conclusion

The project aims to establish an interoperable AI network across healthcare institutions, connecting existing infrastructures and AI services to enhance clinical outcomes.

Key Points

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.

Citation Format

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

Zusammenfassung

Hintergrund

Die Integration von Künstlicher Intelligenz (KI) im Gesundheitswesen verändert die klinische Entscheidungsfindung, Patientenergebnisse und Abläufe. KI-Inferenz, die Anwendung trainierter Modelle auf neue Daten, ist zentral für diese Entwicklung, wobei Cloud-Infrastrukturen skalierbare KI-Einsätze ermöglichen. Die Open Medical Inference (OMI)-Plattform demokratisiert den Zugang zu KI-Diensten durch offene Protokolle und standardisierte Datenformate für einen nahtlosen, interoperablen Austausch von Gesundheitsdaten. OMI integriert Standards wie FHIR und DICOMweb zur Sicherstellung der Interoperabilität zwischen Gesundheitseinrichtungen und KI-Diensten und fördert die ethische KI-Nutzung durch ein Governance-Framework für Datenschutz, Transparenz und Fairness.

Methode

Die Umsetzung von OMI erfolgt in Arbeitspaketen, die technische und ethische Aspekte abdecken. Dazu gehören die Erweiterung des Kerndatensatzes der Medizininformatik-Initiative (MII) für medizinische Bildgebung, die Entwicklung von Infrastrukturen für KI-Inferenz und die Erstellung eines Open-Source-DICOMweb-Adapters für Altsysteme. Standardisierte Datenformate gewährleisten Interoperabilität, während das KI-Governance-Framework Vertrauen und verantwortungsvolle Nutzung stärkt.

Schlussfolgerung

Das Projekt zielt darauf ab, ein interoperables KI-Netzwerk für Gesundheitseinrichtungen zu etablieren, bestehende Infrastrukturen und KI-Dienste zu verknüpfen und so die klinischen Ergebnisse zu verbessern.

Kernaussagen

OMI entwickelt offene Protokolle und standardisierte Datenformate für den nahtlosen Austausch von Gesundheitsdaten.
Die Integration mit FHIR und DICOMweb gewährleistet Interoperabilität zwischen Gesundheitssystemen und KI-Diensten.
Ein Governance-Framework adressiert Datenschutz, Transparenz und Fairness in der KI-Nutzung.
Arbeitspakete konzentrieren sich auf Datensatzerweiterung, Infrastrukturerstellung und Altsystemintegration.
Das Projekt strebt ein skalierbares, sicheres und interoperables KI-Netzwerk im Gesundheitswesen an.

Keywords

AI Ethics in Healthcare - Artificial Intelligence in Healthcare - Healthcare Data Interoperability - Medical Inference

Volltext

Referenzen

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