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000281520 0247_ $$2doi$$a10.3233/SHTI251485
000281520 0247_ $$2ISSN$$a0926-9630
000281520 0247_ $$2ISSN$$a1879-8365
000281520 037__ $$aDZNE-2025-01138
000281520 041__ $$aEnglish
000281520 082__ $$a300
000281520 1001_ $$aHübner, Ursula H.$$b0$$eEditor
000281520 1112_ $$a 24th Special Topic Conference (STC 2025) of the European Federation for Medical Informatics (EFMI)$$cOsnabrück$$d2025-10-20 - 2025-10-22$$wGermany
000281520 245__ $$aEvaluation of Clinical AI-Based Diagnostic Solutions – A Multiperspective, Interdisciplinary Approach
000281520 260__ $$aAmsterdam$$bIOS Press$$c2025
000281520 29510 $$aGood Evaluation - Better Digital Health / Hübner, Ursula H. (Editor) ; : IOS Press, , ; ISSN: 09269630=18798365 ; ISBN: 9781643686295 ; doi:10.3233/SHTI251485
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000281520 4900_ $$aStudies in Health Technology and Informatics$$v332
000281520 520__ $$aThe 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.
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000281520 650_7 $$2Other$$aArtificial Intelligence
000281520 650_7 $$2Other$$aclinical diagnostics
000281520 650_7 $$2Other$$aevaluation
000281520 650_7 $$2Other$$aimplementation
000281520 650_2 $$2MeSH$$aArtificial Intelligence
000281520 650_2 $$2MeSH$$aHumans
000281520 650_2 $$2MeSH$$aDiagnosis, Computer-Assisted: methods
000281520 7001_ $$aLiebe, Jan-David$$b1$$eEditor
000281520 7001_ $$aBenis, Arriel$$b2$$eEditor
000281520 7001_ $$aEgbert, Nicole$$b3$$eEditor
000281520 7001_ $$aEngelsma, Thomas$$b4$$eEditor
000281520 7001_ $$aGallos, Parisis$$b5$$eEditor
000281520 7001_ $$aFlemming, Daniel$$b6$$eEditor
000281520 7001_ $$aLichtner, Valentina$$b7$$eEditor
000281520 7001_ $$aMarcilly, Romaric$$b8$$eEditor
000281520 7001_ $$aTamburis, Oscar$$b9$$eEditor
000281520 7001_ $$aVillumsen, Sidsel$$b10$$eEditor
000281520 7001_ $$00000-0002-4448-6853$$aKaropka, Thomas$$b11
000281520 7001_ $$aØstervig Byskov, Carina$$b12
000281520 7001_ $$0P:(DE-2719)2810283$$aDyrba, Martin$$b13$$eLast author
000281520 773__ $$a10.3233/SHTI251485
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