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@ARTICLE{Lennerz:212479,
      author       = {J. K. Lennerz and R. Salgado and G. E. Kim and S. J.
                      Sirintrapun and J. C. Thierauf$^*$ and A. Singh and I.
                      Indave and A. Bard and S. E. Weissinger and Y. K. Heher and
                      M. E. de Baca and I. A. Cree and S. Bennett and A. Carobene
                      and T. Ozben and L. L. Ritterhouse},
      title        = {{D}iagnostic quality model ({DQM}): an integrated framework
                      for the assessment of diagnostic quality when using
                      {AI}/{ML}.},
      journal      = {Clinical chemistry and laboratory medicine},
      volume       = {61},
      number       = {4},
      issn         = {1434-6621},
      address      = {Berlin [u.a.]},
      publisher    = {De Gruyter},
      reportid     = {DKFZ-2023-00187},
      pages        = {544-557},
      year         = {2023},
      note         = {Department of Otorhinolaryngology, Head and Neck Surgery,
                      German Cancer Research Center (DKFZ), / 2023 Jan
                      25;61(4):544-557},
      abstract     = {Laboratory medicine has reached the era where promises of
                      artificial intelligence and machine learning (AI/ML) seem
                      palpable. Currently, the primary responsibility for
                      risk-benefit assessment in clinical practice resides with
                      the medical director. Unfortunately, there is no tool or
                      concept that enables diagnostic quality assessment for the
                      various potential AI/ML applications. Specifically, we noted
                      that an operational definition of laboratory diagnostic
                      quality - for the specific purpose of assessing AI/ML
                      improvements - is currently missing.A session at the 3rd
                      Strategic Conference of the European Federation of
                      Laboratory Medicine in 2022 on 'AI in the Laboratory of the
                      Future' prompted an expert roundtable discussion. Here we
                      present a conceptual diagnostic quality framework for the
                      specific purpose of assessing AI/ML implementations.The
                      presented framework is termed diagnostic quality model (DQM)
                      and distinguishes AI/ML improvements at the test, procedure,
                      laboratory, or healthcare ecosystem level. The operational
                      definition illustrates the nested relationship among these
                      levels. The model can help to define relevant objectives for
                      implementation and how levels come together to form coherent
                      diagnostics. The affected levels are referred to as scope
                      and we provide a rubric to quantify AI/ML improvements while
                      complying with existing, mandated regulatory standards. We
                      present 4 relevant clinical scenarios including multi-modal
                      diagnostics and compare the model to existing quality
                      management systems.A diagnostic quality model is essential
                      to navigate the complexities of clinical AI/ML
                      implementations. The presented diagnostic quality framework
                      can help to specify and communicate the key implications of
                      AI/ML solutions in laboratory diagnostics.},
      keywords     = {artificial intelligence (Other) / biomarker (Other) /
                      laboratory medicine (Other) / machine learning (Other) /
                      regulatory science (Other)},
      cin          = {E221},
      ddc          = {610},
      cid          = {I:(DE-He78)E221-20160331},
      pnm          = {315 - Bildgebung und Radioonkologie (POF4-315)},
      pid          = {G:(DE-HGF)POF4-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:36696602},
      doi          = {10.1515/cclm-2022-1151},
      url          = {https://inrepo02.dkfz.de/record/212479},
}