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@INPROCEEDINGS{Hbner:281520,
      author       = {Karopka, Thomas and Østervig Byskov, Carina and Dyrba,
                      Martin},
      editor       = {Hübner, Ursula H. and Liebe, Jan-David and Benis, Arriel
                      and Egbert, Nicole and Engelsma, Thomas and Gallos, Parisis
                      and Flemming, Daniel and Lichtner, Valentina and Marcilly,
                      Romaric and Tamburis, Oscar and Villumsen, Sidsel},
      title        = {{E}valuation of {C}linical {AI}-{B}ased {D}iagnostic
                      {S}olutions – {A} {M}ultiperspective, {I}nterdisciplinary
                      {A}pproach},
      volume       = {332},
      address      = {Amsterdam},
      publisher    = {IOS Press},
      reportid     = {DZNE-2025-01138},
      series       = {Studies in Health Technology and Informatics},
      pages        = {7 - 11},
      year         = {2025},
      comment      = {Good Evaluation - Better Digital Health / Hübner, Ursula
                      H. (Editor) ; : IOS Press, , ; ISSN: 09269630=18798365 ;
                      ISBN: 9781643686295 ; doi:10.3233/SHTI251485},
      booktitle     = {Good Evaluation - Better Digital
                       Health / Hübner, Ursula H. (Editor) ;
                       : IOS Press, , ; ISSN:
                       09269630=18798365 ; ISBN: 9781643686295
                       ; doi:10.3233/SHTI251485},
      abstract     = {The primary goal of developing new clinical diagnostic
                      solutions is to create value for healthcare. The rapid rise
                      of artificial intelligence (AI)-based diagnostics has led to
                      a surge in publications and, to a lesser extent,
                      market-ready tools. Clinicians must now integrate these
                      innovations to manage increasing data volumes, making it
                      challenging to assess the added value of new tools in the
                      diagnostic workflow.The INTERREG Baltic Sea Region project
                      'Clinical Artificial Intelligence-Based Diagnostics (CAIDX)'
                      developed a comprehensive blueprint guiding the process from
                      identifying clinical needs to implementing certified AI
                      products in diagnostics. The approach emphasizes systematic
                      evaluation at each development stage and throughout the AI
                      solution's lifecycle, incorporating diverse stakeholder
                      perspectives and a range of evaluation methodologies.The
                      CAIDX project produced the 'Clinical AI-Pathway,' an
                      end-to-end framework for integrating AI-based diagnostic
                      tools. This framework provides methodologies and tools for
                      systematic evaluation at all stages, ensuring alignment with
                      clinical needs and rigorous assessment of value.Systematic,
                      multi-perspective evaluation is crucial for successfully
                      integrating AI diagnostics into clinical practice. The
                      'Clinical AI-Pathway' framework offers a structured method
                      for assessing and implementing AI solutions, supporting
                      their value-driven adoption in healthcare. The framework,
                      available at ClinicalAI.eu, aims to facilitate broader and
                      more effective use of AI in clinical diagnostics.},
      month         = {Oct},
      date          = {2025-10-20},
      organization  = {24th Special Topic Conference (STC
                       2025) of the European Federation for
                       Medical Informatics (EFMI), Osnabrück
                       (Germany), 20 Oct 2025 - 22 Oct 2025},
      keywords     = {Artificial Intelligence / Humans / Diagnosis,
                      Computer-Assisted: methods / Artificial Intelligence (Other)
                      / clinical diagnostics (Other) / evaluation (Other) /
                      implementation (Other)},
      cin          = {AG Teipel},
      ddc          = {300},
      cid          = {I:(DE-2719)1510100},
      pnm          = {353 - Clinical and Health Care Research (POF4-353)},
      pid          = {G:(DE-HGF)POF4-353},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      doi          = {10.3233/SHTI251485},
      url          = {https://pub.dzne.de/record/281520},
}