% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Pelka:305006,
      author       = {O. Pelka and S. Sigle and P. Werner and S. T. Schweizer and
                      A. Iancu and L. Scherer and N. A. Kamzol and J. H. Eil and
                      T. Apfelbacher and D. Seletkov and T. Susetzky and M. S. May
                      and A. M. Bucher and C. Fegeler and M. Boeker and R.
                      Braren$^*$ and H.-U. Prokosch and F. Nensa},
      title        = {{D}emocratizing {AI} in {H}ealthcare with {O}pen {M}edical
                      {I}nference ({OMI}): {P}rotocols, {D}ata {E}xchange, and
                      {AI} {I}ntegration. [{D}emokratisierung von {KI} im
                      {G}esundheitswesen mit {O}pen {M}edical {I}nference ({OMI}):
                      {P}rotokolle, {D}atenaustausch und {KI}-{I}ntegration].},
      journal      = {RöFo},
      volume       = {nn},
      issn         = {1438-9029},
      address      = {Stuttgart [u.a.]},
      publisher    = {Thieme},
      reportid     = {DKFZ-2025-01997},
      pages        = {nn},
      year         = {2025},
      note         = {epub},
      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.},
      subtyp        = {Review Article},
      cin          = {MU01},
      ddc          = {610},
      cid          = {I:(DE-He78)MU01-20160331},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:41022108},
      doi          = {10.1055/a-2651-6653},
      url          = {https://inrepo02.dkfz.de/record/305006},
}