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

Obioma Pelka1,2, Stefan Sigle3, Patrick Werner3

  • 1Institute of Artificial intelligence in Medicine, University Hospital Essen, Essen, Germany.

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Summary
This summary is machine-generated.

The Open Medical Inference (OMI) platform enhances healthcare by enabling interoperable AI integration. It uses open protocols and standards like FHIR and DICOMweb for secure data exchange and ethical AI deployment.

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Health Data Interoperability

Background:

  • Artificial intelligence (AI) integration is revolutionizing healthcare, impacting clinical decisions, patient results, and operational workflows.
  • AI inference, the application of trained models to new data, is a key component of this transformation, supported by scalable cloud infrastructures.
  • The need for standardized, interoperable data exchange and ethical AI governance is paramount for widespread adoption.

Purpose of the Study:

  • To introduce the Open Medical Inference (OMI) platform, designed to democratize access to AI in healthcare.
  • To establish an interoperable AI network connecting healthcare institutions and AI services.
  • To foster ethical AI use through a robust governance framework.

Main Methods:

  • Development of open protocols and standardized data formats for seamless healthcare data exchange.
  • Integration with existing healthcare standards such as FHIR (Fast Healthcare Interoperability Resources) and DICOMweb.
  • Implementation structured into work packages focusing on dataset expansion, AI inference infrastructure, and legacy system integration via an open-source DICOMweb adapter.

Main Results:

  • Successful creation of a platform promoting interoperability between healthcare systems and AI services.
  • Establishment of a governance framework to address critical ethical considerations including privacy, transparency, and fairness.
  • Development of infrastructure and tools enabling scalable, secure, and ethical AI deployment in healthcare settings.

Conclusions:

  • The OMI platform facilitates democratized access to AI in healthcare through open protocols and standardized data exchange.
  • Integration of OMI with FHIR and DICOMweb ensures seamless interoperability, enhancing the connection between healthcare institutions and AI services.
  • The project successfully lays the groundwork for a scalable, secure, and trustworthy AI network within the healthcare ecosystem, promoting responsible innovation.